In the Swallowing Rehabilitation Research Laboratory at the Toronto Rehabilitation Institute, we have developed a treatment protocol using sEMG biofeedback for patients with neurogenic dysphagia, for whom either the effortful swallow and/or the Mendelsohn maneuver are indicated. The protocol uses software that has been developed in conjunction with the Biofeedback Foundation of Europe (www.bfe.org), displaying a sEMG signal representing the composite muscle activity of the floor-of-mouth muscles (likely including the mylohyoid, geniohyoid, anterior belly digastric and genioglossus muscles). Patients who complete this protocol practice approximately 60 saliva swallows per treatment session, over 20-24 sessions, scheduled twice weekly. Tasks include amplitude target practice (for non-effortful and effortful swallows) and muscle contraction prolongation (for the Mendelsohn maneuver). Functional outcomes are measured using a standardized videofluoroscopic swallowing study (VFSS), comparing post-treatment function to baseline performance. Two videofluoroscopic measures are of particular interest: 1) the Penetration-Aspiration Scale, an 8-point ordinal scale measuring the depth of airway invasion; and 2) measures of post-swallow residue, captured using a 4-point ordinal scale reflecting the degree to which the available space in the pyriform sinuses is filled with residue (Eisenhuber et al., 2002). In this chapter, we will share sEMG and outcome data from several

patients who have completed treatment using sEMG biofeedback.

measure and display the duration of muscle contraction used in this task.

### **2.1 Instrumentation**

An adhesive triode electrode patch is attached under the participant's chin with the positive and negative electrodes positioned just lateral to midline, anterior to the hyoid bone (Figure 2). The reference (ground) electrode is positioned away from the muscles of interest. The electrodes are attached to an EMG device (MyoTrac Infiniti, Thought Technology, Montreal) using a sensor cable and the signal from the device is registered on a computer using specialized software (BioGraph Infiniti, Thought Technology, Montreal).

Fig. 2. Electrode attachment under the chin, just lateral to midline, with the reference electrode off to one side.

#### **2.2 Treatment protocol**

We recommend that a treatment session should include 60 swallows, practiced in 12 sets of 5 swallows. The protocol involves a progression of tasks from "regular effort saliva swallows" (RESS) to "effortful saliva swallows" (ESS) and, where indicated, "Mendelsohn Maneuver saliva swallows" (MM), as illustrated in Figure 3.

Step 1 in the protocol is a check of the submental sEMG signal to make sure that the electrodes are correctly displaying activity of the floor of mouth musculature. Once the electrodes have been positioned and connected, the clinician should ask the patient to perform some tasks that are expected to display variations in signal amplitude of the submental muscles, such as jaw opening.

Step 2 in the protocol involves a baseline measurement of sEMG amplitude during regular-effort saliva swallows. For this task, we recommend that the patient to produce one saliva swallow every 30 seconds, followed by a rest period. If the patient has extreme difficulty initiating a saliva swallow, a tiny amount of water may be taken to provide a stimulus. The 30-second cycle of resting and swallowing is repeated 5 times, allowing a measurement of the patient's average saliva swallow amplitudes for that session. All

Electromyography as a Biofeedback Tool for Rehabilitating Swallowing Muscle Function 315

Step 3 in the treatment protocol involves further practice of RESS. The protocol proceeds in a similar manner to the previous baseline task, with alternating cycles of 20-second-long rests and 10-second intervals in which a saliva swallow should be produced. In contrast to the previous task, however, this step in the protocol involves establishing a target amplitude for the peak of each swallow. This target is set between 90% and 110% of the mean peak amplitude measured in the baseline task, and a target line appears on the screen. Success on this task is defined as a mean peak amplitude within 10% of target across a series of 5

Step 4 in the treatment protocol proceeds to the practice of effortful saliva swallows (ESS). Here the goal is to contract the swallowing muscles with greater effort so that the signal amplitude reaches higher values. We begin working at 110% of the mean peak amplitude measured in the baseline reference task. Once the patient can produce swallows within 10% of this target on two successive series of 5 saliva swallows, the challenge level is increased incrementally by steps of 10%. We recommend increasing the difficulty in steps of 10%. Figure 4 displays a typical 2 ½ minute recording of submental muscle activity during

Fig. 4. A series of 5 effortful swallows is shown, one swallow per 30 seconds. The dashed black line shows an amplitude target line set at 40 microvolts. Success in reaching or

surpassing this target is illustrated for the 3rd and 4th ESS of the series.

repeated saliva swallows.

practice of the ESS task.

amplitude targets for other tasks in the session are then set relative to the baseline swallow measures for the session. This ensures that artifacts in sEMG amplitude due to variations in electrode placement and contact across sessions and participants do not interfere with the treatment protocol.

Fig. 3. Flow-chart of a typical sEMG biofeedback session for swallowing rehabilitation.

amplitude targets for other tasks in the session are then set relative to the baseline swallow measures for the session. This ensures that artifacts in sEMG amplitude due to variations in electrode placement and contact across sessions and participants do not

Fig. 3. Flow-chart of a typical sEMG biofeedback session for swallowing rehabilitation.

interfere with the treatment protocol.

Step 3 in the treatment protocol involves further practice of RESS. The protocol proceeds in a similar manner to the previous baseline task, with alternating cycles of 20-second-long rests and 10-second intervals in which a saliva swallow should be produced. In contrast to the previous task, however, this step in the protocol involves establishing a target amplitude for the peak of each swallow. This target is set between 90% and 110% of the mean peak amplitude measured in the baseline task, and a target line appears on the screen. Success on this task is defined as a mean peak amplitude within 10% of target across a series of 5 repeated saliva swallows.

Step 4 in the treatment protocol proceeds to the practice of effortful saliva swallows (ESS). Here the goal is to contract the swallowing muscles with greater effort so that the signal amplitude reaches higher values. We begin working at 110% of the mean peak amplitude measured in the baseline reference task. Once the patient can produce swallows within 10% of this target on two successive series of 5 saliva swallows, the challenge level is increased incrementally by steps of 10%. We recommend increasing the difficulty in steps of 10%. Figure 4 displays a typical 2 ½ minute recording of submental muscle activity during practice of the ESS task.

Fig. 4. A series of 5 effortful swallows is shown, one swallow per 30 seconds. The dashed black line shows an amplitude target line set at 40 microvolts. Success in reaching or surpassing this target is illustrated for the 3rd and 4th ESS of the series.

Electromyography as a Biofeedback Tool for Rehabilitating Swallowing Muscle Function 317

evidence of reduced hyolaryngeal excursion or post-swallow residues in the pyriform sinuses on a standardized VFSS examination. In a retrospective review of patients, we were able to find several cases in which fairly complete sEMG signal data were available, together with pre- and post-treatment videofluoroscopy recordings. Complete data of this nature are not always available, due to signal quality issues, or decisions by the treating clinician to

In this chapter, we will use the available data to explore five questions regarding the use of

1. How variable are sEMG amplitudes for the submental muscles in regular-effortsaliva

2. To what extent do effortful swallow sEMG peak amplitudes increase over time in

3. Do videofluoroscopic measures of swallowing function improve with increases in

4. To what extent does the duration of muscle contraction increase in patients who

5. Do videofluoroscopic measures of swallowing function improve with increases in the duration of muscle contraction secondary to practice of the Mendelsohn Maneuver?

To answer this question, we extracted sEMG data for the baseline RESS task from the first 8 available treatment sessions in 8 patients and 2 healthy, young controls (one female, one male). Signals were first visually inspected for obvious artifact, combined with a review of notes recorded by the clinician during each session; where necessary, portions of the signal containing artifact were removed using an ignore-segment function in the sEMG software. Amplitude range (peak minus rest) for the RESS baseline task is plotted by participant in Figure 6, with dashed lines showing a one standard deviation confidence interval band

As can be seen from the varying scales on the y-axes in Figure 6, the actual value of the peak-minus-rest amplitude range (in microVolts) varied considerably across participants. The majority of participants showed maximum peak values below 100 microVolts, but this was not true for Control 2, nor for patient participants 4 and 8. Previous literature suggests that sEMG amplitudes may vary as a function of the amount of fatty tissue between the surface of the neck and the underlying muscles; this may have been one of the factors contributing to the higher amplitudes seen in these individuals. Furthermore, it can be seen that both controls and all 8 patient participants showed at least one outlier session, for which the recorded amplitude range fell outside the boundaries of a one standard deviation confidence interval. Minor variations in electrode placement and contact from session to session may contribute to such fluctuations in sEMG amplitude. The presence of fluctuating amplitudes across these data supports the need to normalize all sEMG amplitude values to a within-participant within-session reference value prior to interpretation or groupwise statistical analysis. This is a standard step in our protocol, achieved by recording a series of RESS at the beginning of the session, and subsequently transforming all other amplitude

**3.1 How variable are sEMG amplitudes for the submental muscles in regular-effort**

**saliva swallows across repeated sessions within individual participants?**

swallows across repeated sessions within individual participants?

retain only a portion of the data from a particular session.

sEMG peak amplitudes on effortful swallows?

measures to a percent of the mean baseline RESS range.

practice the Mendelsohn Maneuver?

sEMG biofeedback in dysphagia therapy.

patients with dysphagia?

around the mean.

The final step in the treatment protocol is optional practice of the Mendelsohn Maneuver (MM). The goal of this task is to prolong the muscle contraction associated with swallowing. It is important that the event begins with a real swallow, leading into a sustained muscle contraction. The sEMG profile displayed on the screen is described to typically look like a straight-backed chair, where the signal first reaches a swallowing peak, then drops slightly, and is then maintained for 2-3 seconds (Figure 5). For this task, we recommend using a lower threshold of 30% of the reference amplitude range and measuring the duration of time that the patient maintains contraction above this level.

Fig. 5. A series of 5 Mendelsohn Maneuvers, displaying the characteristic straight-backed chair profile, beginning with a peak amplitude associated with a saliva swallow, and followed by prolongation of muscle contraction for 2-3 seconds, above a lower threshold set by the clinician (recommended value: 30% of regular effort saliva swallow amplitudes).

#### **3. Case reports**

Over the past three years, we have been following patients who participated in this treatment protocol at three hospitals. Patients are selected based on videofluoroscopic

The final step in the treatment protocol is optional practice of the Mendelsohn Maneuver (MM). The goal of this task is to prolong the muscle contraction associated with swallowing. It is important that the event begins with a real swallow, leading into a sustained muscle contraction. The sEMG profile displayed on the screen is described to typically look like a straight-backed chair, where the signal first reaches a swallowing peak, then drops slightly, and is then maintained for 2-3 seconds (Figure 5). For this task, we recommend using a lower threshold of 30% of the reference amplitude range and measuring the duration of time

Fig. 5. A series of 5 Mendelsohn Maneuvers, displaying the characteristic straight-backed chair profile, beginning with a peak amplitude associated with a saliva swallow, and followed by prolongation of muscle contraction for 2-3 seconds, above a lower threshold

Over the past three years, we have been following patients who participated in this treatment protocol at three hospitals. Patients are selected based on videofluoroscopic

set by the clinician (recommended value: 30% of regular effort saliva swallow

amplitudes).

**3. Case reports**

that the patient maintains contraction above this level.

evidence of reduced hyolaryngeal excursion or post-swallow residues in the pyriform sinuses on a standardized VFSS examination. In a retrospective review of patients, we were able to find several cases in which fairly complete sEMG signal data were available, together with pre- and post-treatment videofluoroscopy recordings. Complete data of this nature are not always available, due to signal quality issues, or decisions by the treating clinician to retain only a portion of the data from a particular session.

In this chapter, we will use the available data to explore five questions regarding the use of sEMG biofeedback in dysphagia therapy.

#### **3.1 How variable are sEMG amplitudes for the submental muscles in regular-effort saliva swallows across repeated sessions within individual participants?**

To answer this question, we extracted sEMG data for the baseline RESS task from the first 8 available treatment sessions in 8 patients and 2 healthy, young controls (one female, one male). Signals were first visually inspected for obvious artifact, combined with a review of notes recorded by the clinician during each session; where necessary, portions of the signal containing artifact were removed using an ignore-segment function in the sEMG software. Amplitude range (peak minus rest) for the RESS baseline task is plotted by participant in Figure 6, with dashed lines showing a one standard deviation confidence interval band around the mean.

As can be seen from the varying scales on the y-axes in Figure 6, the actual value of the peak-minus-rest amplitude range (in microVolts) varied considerably across participants. The majority of participants showed maximum peak values below 100 microVolts, but this was not true for Control 2, nor for patient participants 4 and 8. Previous literature suggests that sEMG amplitudes may vary as a function of the amount of fatty tissue between the surface of the neck and the underlying muscles; this may have been one of the factors contributing to the higher amplitudes seen in these individuals. Furthermore, it can be seen that both controls and all 8 patient participants showed at least one outlier session, for which the recorded amplitude range fell outside the boundaries of a one standard deviation confidence interval. Minor variations in electrode placement and contact from session to session may contribute to such fluctuations in sEMG amplitude. The presence of fluctuating amplitudes across these data supports the need to normalize all sEMG amplitude values to a within-participant within-session reference value prior to interpretation or groupwise statistical analysis. This is a standard step in our protocol, achieved by recording a series of RESS at the beginning of the session, and subsequently transforming all other amplitude measures to a percent of the mean baseline RESS range.

Electromyography as a Biofeedback Tool for Rehabilitating Swallowing Muscle Function 319

**3.2 To what extent do effortful swallow sEMG peak amplitudes increase over time in**

Participant Gender Age Primary Diagnosis

8 M 69 Spinal cord injury with tetraplegia Table 1. Demographic information regarding the cases discussed in this chapter.

4 M 75 Stroke

6 F 88 Stroke 7 M 88 Stroke

Six of the patient participants in this case series had available data for effortful swallows from at least 5 treatment sessions. Table 1 provides demographic information regarding

2 M 49 Acoustic neuroma; damage to adjacent cranial nerves

5 M 68 Sudden onset of dysphagia with vocal fold paresis

For the purposes of calculating change in effortful swallow performance, all ESS amplitude data were first transformed into percentage values relative to RESS range, using the method described in section 3.1 (above). A baseline reference range for effortful swallows was then established by calculating the mean and standard deviation of all ESS values in the first two treatment sessions for each participant. As shown in Figure 7 one (grey) and two (black) standard deviation bands were plotted around the baseline mean on a control chart for each participant. Mean ESS amplitude range was then plotted for

Standard deviation banding methods for single subject analysis usually specify a criterion for concluding that a participant is showing change, namely the demonstration of two successive data points falling beyond a specified standard deviation boundary (Nourbakhsh & Ottenbacher, 1994). Applying a two standard deviation criterion, Figure 7 shows that participant 2 demonstrates an apparent increase in ESS values at probes 3 and 4, but this trend was not sustained and reverses at probe 5. The values seen at probe 4 are also sufficiently outlying to suggest the possibility that some sort of measurement artifact may have been affecting those data. Participants 5 and 7 both show a general trend towards increasing ESS values, but these changes fail to cross either the one or two standard deviation boundaries across more than a single session. The outlier value for probe 14 in participant 7's data is sufficiently larger than other probes to suggest an artifact effect. Participant 8 shows an overall upward trend in ESS values with the final two probes sitting at or above the two standard deviation bandwidth. Participant 6 shows very little evidence of change across treatment, remaining within the standard deviation bands. Participant 4 shows a general downward trend in ESS values across probes, but with values consistently falling within a single standard deviation band. Based on these graphs, it would appear that only participant 8 showed a convincing increase in effortful saliva swallow amplitudes, expressed as a percent of RESS range. Caution must, however, be used in accepting this interpretation, given the uneven number of data points available

**patients with dysphagia?**

these 6 participants.

each treatment session.

across participants.

Fig. 6. Regular effort saliva swallow (RESS) amplitude range across repeated recording sessions in two healthy controls and eight patients with dysphagia.

Fig. 6. Regular effort saliva swallow (RESS) amplitude range across repeated recording

sessions in two healthy controls and eight patients with dysphagia.

#### **3.2 To what extent do effortful swallow sEMG peak amplitudes increase over time in patients with dysphagia?**

Six of the patient participants in this case series had available data for effortful swallows from at least 5 treatment sessions. Table 1 provides demographic information regarding these 6 participants.

Table 1. Demographic information regarding the cases discussed in this chapter.

For the purposes of calculating change in effortful swallow performance, all ESS amplitude data were first transformed into percentage values relative to RESS range, using the method described in section 3.1 (above). A baseline reference range for effortful swallows was then established by calculating the mean and standard deviation of all ESS values in the first two treatment sessions for each participant. As shown in Figure 7 one (grey) and two (black) standard deviation bands were plotted around the baseline mean on a control chart for each participant. Mean ESS amplitude range was then plotted for each treatment session.

Standard deviation banding methods for single subject analysis usually specify a criterion for concluding that a participant is showing change, namely the demonstration of two successive data points falling beyond a specified standard deviation boundary (Nourbakhsh & Ottenbacher, 1994). Applying a two standard deviation criterion, Figure 7 shows that participant 2 demonstrates an apparent increase in ESS values at probes 3 and 4, but this trend was not sustained and reverses at probe 5. The values seen at probe 4 are also sufficiently outlying to suggest the possibility that some sort of measurement artifact may have been affecting those data. Participants 5 and 7 both show a general trend towards increasing ESS values, but these changes fail to cross either the one or two standard deviation boundaries across more than a single session. The outlier value for probe 14 in participant 7's data is sufficiently larger than other probes to suggest an artifact effect. Participant 8 shows an overall upward trend in ESS values with the final two probes sitting at or above the two standard deviation bandwidth. Participant 6 shows very little evidence of change across treatment, remaining within the standard deviation bands. Participant 4 shows a general downward trend in ESS values across probes, but with values consistently falling within a single standard deviation band. Based on these graphs, it would appear that only participant 8 showed a convincing increase in effortful saliva swallow amplitudes, expressed as a percent of RESS range. Caution must, however, be used in accepting this interpretation, given the uneven number of data points available across participants.

Electromyography as a Biofeedback Tool for Rehabilitating Swallowing Muscle Function 321

texture tolerance rather than using objective blinded rating of standardized videofluoroscopies. It remains unclear whether specific physiological parameters in swallowing that are logically linked to the strength or duration of suprahyoid muscle

Our understanding of the effortful swallow is that it should enhance bolus propulsion and clearance through the oropharynx in swallowing (Bülow et al., 1999; Bülow et al., 2001; Hind et al., 2001). Therefore, the primary videofluoroscopic parameter expected to respond to this exercise would be post-swallow residue. Other parameters that are logically related to this treatment might include the range of hyolaryngeal excursion and measures of bolus flow velocity or kinematics (Clave et al., 2006; Hind et al., 2001). In our series, pre- and post-treatment videofluoroscopy recordings were available for analysis for four of the six patients discussed previously under question 2 (participants 2, 4, 7 and 8). These recordings were spliced into clips containing the swallowing sequence for each bolus presented. The first two thin liquid and two thick liquid swallow clips were then arranged in random order and rated by two experienced speech-language pathologists, blinded to the identity of the patient participant and the timepoint of each swallow. We chose to rate post-swallow residues in the valleculae and pyriform sinuses using 4-point ordinal rating scales developed by Eisenhuber and colleagues (Eisenhuber et al., 2002). We also used the 8-point Penetration-Aspiration Scale (Rosenbek et al., 1996) to measure airway invasion, although this feature was not necessarily expected to change as a

Scores for post-swallow vallecular residue remained unchanged for all participants across treatment. This finding is not particularly surprising, given that vallecular residue is thought to arise primarily from weak tongue propulsion forces in swallowing, and these were not directly targeted in therapy. Pyriform sinus residues, however, were expected to respond to block practice of the effortful swallow. In this respect, participant 7 showed a marked improvement with thickened liquids, from pre-treatment values of 3 (severe) to post-treatment values of 0 (no residue). Participant 4 showed a slight improvement on this variable for thin liquids, shifting from a severe to a moderate rating. Participants 2 and 8 showed no changes in pyriform sinus residue measures, which remained in the moderatesevere range for both thin and thick liquid stimuli. The change seen in participant 7 correlates with the overall upward trend seen in this participant's effortful saliva swallow strength, as indexed by sEMG amplitude data (refer back to Figure 7). Participant 4, however, did not display a noticeable increase in effortful saliva swallow sEMG amplitudes (Figure 7), hence the apparent change in residue scores is not obviously explained by their sEMG measures. Similarly, it is important to note that participant 8, who displayed noticeable increases in effortful saliva swallowing amplitudes (Figure 7) showed no measurable change in swallowing residues, thus challenging the assumption that an

Penetration-Aspiration Scale scores were not seriously impaired at baseline in this subgroup of four patients. The worst baseline performance on this measure was seen in participant 7, who displayed a score of 3 (i.e., airway invasion into the supraglottic space, above the vocal folds) with thickened liquid stimuli. Participant 4 also displayed airway invasion to this level with both thin and thickened stimuli, but this was noted to clear spontaneously (score of 2). Post-treatment, all four participants showed normal scores on

contraction change following such treatment.

association can be expected between these measures.

function of treatment.

Fig. 7. Charts showing the standard deviation banding method of monitoring change in effortful saliva swallow (ESS) amplitudes, expressed as a percentage of regular effort saliva swallow (RESS) reference range, in six patients with dysphagia. Light-grey dashed horizontal lines represent a 1 standard deviation confidence interval around the patient's baseline ESS performance. Black dashed horizontal lines represent a 2 standard deviation confidence interval. Change is considered to have occurred when two consecutive data points fall outside the 2 standard deviation confidence interval boundaries.

#### **3.3 Do videofluoroscopic measures of swallowing function improve with increases in sEMG peak amplitudes on effortful swallows?**

The literature on swallowing intervention contains two impressive examples of case series, in which patients who have practiced effortful swallows and/or Mendelsohn maneuvers using sEMG biofeedback have shown dramatic functional improvement (Crary, 1995; Crary & Groher, 2000; Crary et al., 2004; Huckabee & Cannito, 1999). However, in these studies, change has been reported in terms of clinical measures of diet

Fig. 7. Charts showing the standard deviation banding method of monitoring change in effortful saliva swallow (ESS) amplitudes, expressed as a percentage of regular effort saliva

**3.3 Do videofluoroscopic measures of swallowing function improve with increases in**

The literature on swallowing intervention contains two impressive examples of case series, in which patients who have practiced effortful swallows and/or Mendelsohn maneuvers using sEMG biofeedback have shown dramatic functional improvement (Crary, 1995; Crary & Groher, 2000; Crary et al., 2004; Huckabee & Cannito, 1999). However, in these studies, change has been reported in terms of clinical measures of diet

swallow (RESS) reference range, in six patients with dysphagia. Light-grey dashed horizontal lines represent a 1 standard deviation confidence interval around the patient's baseline ESS performance. Black dashed horizontal lines represent a 2 standard deviation confidence interval. Change is considered to have occurred when two consecutive data

points fall outside the 2 standard deviation confidence interval boundaries.

**sEMG peak amplitudes on effortful swallows?**

texture tolerance rather than using objective blinded rating of standardized videofluoroscopies. It remains unclear whether specific physiological parameters in swallowing that are logically linked to the strength or duration of suprahyoid muscle contraction change following such treatment.

Our understanding of the effortful swallow is that it should enhance bolus propulsion and clearance through the oropharynx in swallowing (Bülow et al., 1999; Bülow et al., 2001; Hind et al., 2001). Therefore, the primary videofluoroscopic parameter expected to respond to this exercise would be post-swallow residue. Other parameters that are logically related to this treatment might include the range of hyolaryngeal excursion and measures of bolus flow velocity or kinematics (Clave et al., 2006; Hind et al., 2001). In our series, pre- and post-treatment videofluoroscopy recordings were available for analysis for four of the six patients discussed previously under question 2 (participants 2, 4, 7 and 8). These recordings were spliced into clips containing the swallowing sequence for each bolus presented. The first two thin liquid and two thick liquid swallow clips were then arranged in random order and rated by two experienced speech-language pathologists, blinded to the identity of the patient participant and the timepoint of each swallow. We chose to rate post-swallow residues in the valleculae and pyriform sinuses using 4-point ordinal rating scales developed by Eisenhuber and colleagues (Eisenhuber et al., 2002). We also used the 8-point Penetration-Aspiration Scale (Rosenbek et al., 1996) to measure airway invasion, although this feature was not necessarily expected to change as a function of treatment.

Scores for post-swallow vallecular residue remained unchanged for all participants across treatment. This finding is not particularly surprising, given that vallecular residue is thought to arise primarily from weak tongue propulsion forces in swallowing, and these were not directly targeted in therapy. Pyriform sinus residues, however, were expected to respond to block practice of the effortful swallow. In this respect, participant 7 showed a marked improvement with thickened liquids, from pre-treatment values of 3 (severe) to post-treatment values of 0 (no residue). Participant 4 showed a slight improvement on this variable for thin liquids, shifting from a severe to a moderate rating. Participants 2 and 8 showed no changes in pyriform sinus residue measures, which remained in the moderatesevere range for both thin and thick liquid stimuli. The change seen in participant 7 correlates with the overall upward trend seen in this participant's effortful saliva swallow strength, as indexed by sEMG amplitude data (refer back to Figure 7). Participant 4, however, did not display a noticeable increase in effortful saliva swallow sEMG amplitudes (Figure 7), hence the apparent change in residue scores is not obviously explained by their sEMG measures. Similarly, it is important to note that participant 8, who displayed noticeable increases in effortful saliva swallowing amplitudes (Figure 7) showed no measurable change in swallowing residues, thus challenging the assumption that an association can be expected between these measures.

Penetration-Aspiration Scale scores were not seriously impaired at baseline in this subgroup of four patients. The worst baseline performance on this measure was seen in participant 7, who displayed a score of 3 (i.e., airway invasion into the supraglottic space, above the vocal folds) with thickened liquid stimuli. Participant 4 also displayed airway invasion to this level with both thin and thickened stimuli, but this was noted to clear spontaneously (score of 2). Post-treatment, all four participants showed normal scores on

Electromyography as a Biofeedback Tool for Rehabilitating Swallowing Muscle Function 323

Fig. 9. Mendelsohn Maneuver amplitudes, expressed as a percentage of RESS range, plotted over time for participant 1. In contrast to the duration data shown in Figure 8, this plot shows that sEMG amplitude for this task has remained stable without evident change over 11 probes. Participant 3 was a 55-year-old gentleman with dysphagia secondary to cervical spine surgery. He displayed a particular impairment with persistent moderate post-swallow residues, together with reduced excursion of the hyoid and larynx. Although this patient showed a slight upward trend in his Mendelsohn Maneuver duration data, he failed to show a marked change that reached or surpassed the 2 standard deviation band criterion, as illustrated in Figure 10. However, it should be noted that he started treatment with an ability to sustain the maneuver for 2 or more seconds, which is notably longer than the values seen for participant 1. His average sEMG amplitudes show a contrasting result to the

Fig. 10. Mendelsohn Maneuver durations over time for participant 3. In contrast to the data shown for participant 1 in Figure 8, this individual showed stable durations, which failed to exceed the 2 standard deviation band confidence interval boundaries, suggesting that no

change occurred.

the Penetration-Aspiration Scale, with the exception of participant 2 who had a single episode of aspiration (entry of material into the airway below the level of the vocal folds) with a thin liquid stimulus.

#### **3.4 To what extent does the duration of muscle contraction increase in patients who practice the Mendelsohn Maneuver?**

For two of the patients in our retrospective series, the Mendelsohn Maneuver was the primary focus of treatment. As mentioned previously, this technique emphasizes prolongation of muscle contraction. The functional goal of this exercise is to enhance the degree and duration of UES opening, via biomechanical traction forces related to suprahyoid muscle contraction. In our protocol, the sEMG measures associated with performance of the Mendelsohn Maneuver include the time duration (in seconds) for which muscle contraction is maintained above a 30% lower threshold value, calculated relative to the patient's non-effortful saliva swallow sEMG amplitude range in that session. Additionally, we monitor the mean amplitude of sEMG contraction across the entire duration of the maneuver, normalized to a percentage of the patient's RESS range. It should be noted that the duration rather than a high amplitude of muscle contraction is the focus of the exercise.

Participant 1 in our series was an 82-year-old gentleman who displayed a particular impairment in UES opening, with a visible cricopharyngeal prominence suggesting failed muscle relaxation at the sphincter. The onset of his dysphagia had been sudden, and remained medically unexplained. Figure 8 shows that this patient improved in his ability to maintain muscle contraction from 0.5 to 1.23 seconds across 11 probes. Using a standard deviation banding method similar to that described previously, this patient shows convincing evidence of improvement in Mendelsohn Maneuver durations, beginning around probe 5. This contrasts with his average Mendelsohn Maneuver amplitudes, which remained fairly constant around 30% of the RESS range across treatment (Figure 9).

Fig. 8. Change in Mendelsohn Maneuver durations over time for participant 1. This individual showed an increase from baseline durations of 0.5 seconds to 1.23 seconds after 11 probes. The later data points approach the 2 standard deviation band confidence interval boundaries, suggesting that change has occurred.

the Penetration-Aspiration Scale, with the exception of participant 2 who had a single episode of aspiration (entry of material into the airway below the level of the vocal folds)

**3.4 To what extent does the duration of muscle contraction increase in patients who**

For two of the patients in our retrospective series, the Mendelsohn Maneuver was the primary focus of treatment. As mentioned previously, this technique emphasizes prolongation of muscle contraction. The functional goal of this exercise is to enhance the degree and duration of UES opening, via biomechanical traction forces related to suprahyoid muscle contraction. In our protocol, the sEMG measures associated with performance of the Mendelsohn Maneuver include the time duration (in seconds) for which muscle contraction is maintained above a 30% lower threshold value, calculated relative to the patient's non-effortful saliva swallow sEMG amplitude range in that session. Additionally, we monitor the mean amplitude of sEMG contraction across the entire duration of the maneuver, normalized to a percentage of the patient's RESS range. It should be noted that the duration rather than a high amplitude of muscle contraction is the focus of

Participant 1 in our series was an 82-year-old gentleman who displayed a particular impairment in UES opening, with a visible cricopharyngeal prominence suggesting failed muscle relaxation at the sphincter. The onset of his dysphagia had been sudden, and remained medically unexplained. Figure 8 shows that this patient improved in his ability to maintain muscle contraction from 0.5 to 1.23 seconds across 11 probes. Using a standard deviation banding method similar to that described previously, this patient shows convincing evidence of improvement in Mendelsohn Maneuver durations, beginning around probe 5. This contrasts with his average Mendelsohn Maneuver amplitudes, which

remained fairly constant around 30% of the RESS range across treatment (Figure 9).

Fig. 8. Change in Mendelsohn Maneuver durations over time for participant 1. This individual showed an increase from baseline durations of 0.5 seconds to 1.23 seconds after 11 probes. The later data points approach the 2 standard deviation band confidence interval

boundaries, suggesting that change has occurred.

with a thin liquid stimulus.

the exercise.

**practice the Mendelsohn Maneuver?**

Fig. 9. Mendelsohn Maneuver amplitudes, expressed as a percentage of RESS range, plotted over time for participant 1. In contrast to the duration data shown in Figure 8, this plot shows that sEMG amplitude for this task has remained stable without evident change over 11 probes.

Participant 3 was a 55-year-old gentleman with dysphagia secondary to cervical spine surgery. He displayed a particular impairment with persistent moderate post-swallow residues, together with reduced excursion of the hyoid and larynx. Although this patient showed a slight upward trend in his Mendelsohn Maneuver duration data, he failed to show a marked change that reached or surpassed the 2 standard deviation band criterion, as illustrated in Figure 10. However, it should be noted that he started treatment with an ability to sustain the maneuver for 2 or more seconds, which is notably longer than the values seen for participant 1. His average sEMG amplitudes show a contrasting result to the

Fig. 10. Mendelsohn Maneuver durations over time for participant 3. In contrast to the data shown for participant 1 in Figure 8, this individual showed stable durations, which failed to exceed the 2 standard deviation band confidence interval boundaries, suggesting that no change occurred.

Electromyography as a Biofeedback Tool for Rehabilitating Swallowing Muscle Function 325

vertebral body and normalizing all displacement and position-distance measures to the participant's height by transforming position and displacement measures to a percent of the participants C2 to C4 neck length (Steele et al., 2011). These methods correct for differences in x-ray magnification across images and control for differences in participant height. During the process of extracting these images, the timepoints of UES opening and closing

Superior hyoid displacement is thought to arise primarily from contraction of the mylohyoid muscle while anterior displacement is principally achieved through geniohyoid contraction (Pearson et al., 2010). The Mendelsohn Maneuver likely engages both of these muscles; consequently, increases in hyoid excursion in both the superior and anterior direction might reasonably be anticipated as outcomes of this maneuver. Hyoid excursion in this study was measured as a percentage of the C2-C4 anatomical distance, henceforth called a "cervical unit" (CU). Current best evidence in the field suggests that vertical movements that fall below 44% and anterior movements that fall below 27% of this anatomical reference are associated with penetration-aspiration and post-swallow

Participant 1 in our case series began treatment with substantially reduced upward movement of the hyoid (36% for thin liquids and only 22% for thick liquids); his anterior movement was not markedly impaired, falling at 46% and 51% for thin and thick liquids, respectively. Post-treatment, this patient demonstrated almost a doubling in his superior hyoid movement excursion, at 44% (thin) and 43% (thick). Anterior excursion remained stable at 44% in both planes of movement. By contrast, participant 3 began with a substantial reduction in the degree of anterior hyoid excursion at 12% and 16% for thin and thick liquids, respectively. Superior excursion was of less obvious concern, at 45% (thin) and 58% (thick). Post-treatment, anterior excursion had improved to approximately double the baseline values, at 25% (thin) and 36% (thick). Superior excursion remained fairly stable at 49% with thin liquids, but fell to 39% with thick liquids. Thus, it appears that both of these patients achieved improvements in the aspect of hyoid excursion that was most impaired

Participant 1 had average pre-treatment UES opening widths of 16% for thin liquids and 17% for thick liquids. Pre-treatment UES opening durations were 0.5 seconds for thin liquids and 0.7 seconds for thick liquids. Post-treatment his UES opening widths remained stable at 16% for thin liquids but actually decreased to 13% for thick liquids. Post-treatment UES durations increased to 0.6 seconds with thin liquids but remained stable at 0.5 seconds for thick liquids. These changes in durational and spatial measures are best appreciated in conjunction with the functional measures of interest, namely post-swallow residues. Participant 1 showed complete resolution of severe vallecular and pyriform sinus residue with thick liquids. Residues with thin liquids remained unchanged in the mild range. Thus, even though the width of UES opening decreased and durational measures remained stable for thick liquids, this patient was better able to achieve complete bolus transport from the

Participant 3 had average pre-treatment UES opening widths of 19% (CUs) for thin liquids and 22% for thick liquids. Pre-treatment UES opening durations were 0.37 seconds for thin liquids and 0.33 seconds for thick liquids. Post-treatment his UES opening widths decreased to 11% for thin liquids and to 18% for thick liquids. Post-treatment UES durations decreased

were also recorded, allowing the calculation of the duration of UES opening.

following practice of the Mendelsohn Maneuver using sEMG biofeedback.

residues (Steele et al., 2011).

pharynx into the esophagus post treatment.

durational data, with an upward trend that surpassed the 2 standard deviation band criterion at probe 14 (Figure 11). Thus, this patient improved in his ability to sustain a strong level of muscle contraction throughout the duration of the maneuver.

Fig. 11. Mendelsohn Maneuver amplitudes over time for participant 3, normalized to RESS range. In contrast to the durational data shown in Figure 10, an apparent increase in this measure is seen between probes 14 and 17, with data points surpassing the 2 standard deviation band confidence interval boundary.

#### **3.5 Do videofluoroscopic measures of swallowing function improve with increases in the duration of muscle contraction secondary to practice of the Mendelsohn Maneuver?**

The primary physiologic outcomes related to practice of the Mendelsohn Maneuver include upper esophageal sphincter opening width and duration, range and duration of hyoid excursion, and post-swallow pyriform sinus residues. As with the cases discussed previously, we collected pre- and post-treatment videofluoroscopies for participants 1 and 3 using a standardized protocol, and recordings were then spliced into individual swallow clips for blinded rating by expert speech-language pathologists.

From these recordings, we also extracted the following still frame images, which were used to measure UES opening width, and hyoid position and displacement in Image J software:

All spatial measurements were performed in a coordinate system rotated into alignment with the cervical spine, using an origin positioned at the anterior inferior corner of the C4

durational data, with an upward trend that surpassed the 2 standard deviation band criterion at probe 14 (Figure 11). Thus, this patient improved in his ability to sustain a strong

Fig. 11. Mendelsohn Maneuver amplitudes over time for participant 3, normalized to RESS range. In contrast to the durational data shown in Figure 10, an apparent increase in this measure is seen between probes 14 and 17, with data points surpassing the 2 standard

**3.5 Do videofluoroscopic measures of swallowing function improve with increases in**

The primary physiologic outcomes related to practice of the Mendelsohn Maneuver include upper esophageal sphincter opening width and duration, range and duration of hyoid excursion, and post-swallow pyriform sinus residues. As with the cases discussed previously, we collected pre- and post-treatment videofluoroscopies for participants 1 and 3 using a standardized protocol, and recordings were then spliced into individual swallow

From these recordings, we also extracted the following still frame images, which were used to measure UES opening width, and hyoid position and displacement in Image J software:

All spatial measurements were performed in a coordinate system rotated into alignment with the cervical spine, using an origin positioned at the anterior inferior corner of the C4

**the duration of muscle contraction secondary to practice of the Mendelsohn**

clips for blinded rating by expert speech-language pathologists.

b. Frame of maximum superior hyoid excursion c. Frame of maximum anterior hyoid excursion

e. Frame of minimum hyoid position after UES closure

deviation band confidence interval boundary.

**Maneuver?**

a. Frame of UES opening

d. Frame of UES closure

level of muscle contraction throughout the duration of the maneuver.

vertebral body and normalizing all displacement and position-distance measures to the participant's height by transforming position and displacement measures to a percent of the participants C2 to C4 neck length (Steele et al., 2011). These methods correct for differences in x-ray magnification across images and control for differences in participant height. During the process of extracting these images, the timepoints of UES opening and closing were also recorded, allowing the calculation of the duration of UES opening.

Superior hyoid displacement is thought to arise primarily from contraction of the mylohyoid muscle while anterior displacement is principally achieved through geniohyoid contraction (Pearson et al., 2010). The Mendelsohn Maneuver likely engages both of these muscles; consequently, increases in hyoid excursion in both the superior and anterior direction might reasonably be anticipated as outcomes of this maneuver. Hyoid excursion in this study was measured as a percentage of the C2-C4 anatomical distance, henceforth called a "cervical unit" (CU). Current best evidence in the field suggests that vertical movements that fall below 44% and anterior movements that fall below 27% of this anatomical reference are associated with penetration-aspiration and post-swallow residues (Steele et al., 2011).

Participant 1 in our case series began treatment with substantially reduced upward movement of the hyoid (36% for thin liquids and only 22% for thick liquids); his anterior movement was not markedly impaired, falling at 46% and 51% for thin and thick liquids, respectively. Post-treatment, this patient demonstrated almost a doubling in his superior hyoid movement excursion, at 44% (thin) and 43% (thick). Anterior excursion remained stable at 44% in both planes of movement. By contrast, participant 3 began with a substantial reduction in the degree of anterior hyoid excursion at 12% and 16% for thin and thick liquids, respectively. Superior excursion was of less obvious concern, at 45% (thin) and 58% (thick). Post-treatment, anterior excursion had improved to approximately double the baseline values, at 25% (thin) and 36% (thick). Superior excursion remained fairly stable at 49% with thin liquids, but fell to 39% with thick liquids. Thus, it appears that both of these patients achieved improvements in the aspect of hyoid excursion that was most impaired following practice of the Mendelsohn Maneuver using sEMG biofeedback.

Participant 1 had average pre-treatment UES opening widths of 16% for thin liquids and 17% for thick liquids. Pre-treatment UES opening durations were 0.5 seconds for thin liquids and 0.7 seconds for thick liquids. Post-treatment his UES opening widths remained stable at 16% for thin liquids but actually decreased to 13% for thick liquids. Post-treatment UES durations increased to 0.6 seconds with thin liquids but remained stable at 0.5 seconds for thick liquids. These changes in durational and spatial measures are best appreciated in conjunction with the functional measures of interest, namely post-swallow residues. Participant 1 showed complete resolution of severe vallecular and pyriform sinus residue with thick liquids. Residues with thin liquids remained unchanged in the mild range. Thus, even though the width of UES opening decreased and durational measures remained stable for thick liquids, this patient was better able to achieve complete bolus transport from the pharynx into the esophagus post treatment.

Participant 3 had average pre-treatment UES opening widths of 19% (CUs) for thin liquids and 22% for thick liquids. Pre-treatment UES opening durations were 0.37 seconds for thin liquids and 0.33 seconds for thick liquids. Post-treatment his UES opening widths decreased to 11% for thin liquids and to 18% for thick liquids. Post-treatment UES durations decreased

Electromyography as a Biofeedback Tool for Rehabilitating Swallowing Muscle Function 327

Bülow, M.; Olsson, R. & Ekberg, O. (1999). Videomanometric analysis of supraglottic

Bülow, M.; Olsson, R. & Ekberg, O. (2001). Videomanometric analysis of supraglottic

Burnett, T. A.; Mann, E. A.; Cornell, S. A. & Ludlow, C. L. (2003). Laryngeal elevation

Clave, P.; de Kraa, M.; Arreola, V.; Girvent, M.; Farre, R.; Palomera, E. & Serra-Prat, M.

Crary, M. A. (1995). A direct intervention program for chronic neurogenic dysphagia

Crary, M. A.; Carnaby Mann, G. D.; Groher, M. E. & Helseth, E. (2004). Functional benefits

Crary, M. A. & Groher, M. E. (2000). Basic Concepts of Surface Electromyographic

Eisenhuber, E.; Schima, W.; Schober, E.; Pokieser, P.; Stadler, A.; Scharitzer, M. & Oschatz, E.

Hind, J. A.; Nicosia, M. A.; Roecker, E. B.; Carnes, M. L. & Robbins, J. (2001). Comparison of

*Archives of Physical Medicine and Rehabilitation,* Vol.82, No.12, pp. 1661-1665. Huckabee, M. L. & Cannito, M. (1999). Outcomes of swallowing rehabilitation in chronic brainstem dysphagia: a retrospective evaluation. *Dysphagia*, Vol.14, pp. 93-109. Mendelsohn, M. S. & McConnel, F. M. (1987). Function in the pharyngoesophageal segment.

Nourbakhsh, M. R. & Ottenbacher, K. J. (1994). The statistical analysis of single-subject data: a comparative examination. *Physical Therapy,* Vol.74, No.8, pp. 768-776. Pearson, W. G., Jr.; Langmore, S. E.; & Zumwalt, A. C. (2010). Evaluating the Structural

Rosenbek, J. C.; Robbins, J. A.; Roecker, E. B.; Coyle, J. L. & Wood, J. L. (1996). A penetration-

swallow, effortful swallow, and chin tuck in healthy volunteers. *Dysphagia,* Vol.14,

swallow, effortful swallow, and chin tuck in patients with pharyngeal dysfunction.

achieved by neuromuscular stimulation at rest. *Journal of Applied Physiology,* Vol.94,

(2006). The effect of bolus viscosity on swallowing function in neurogenic dysphagia. *Alimentary Pharmacology & Therapeutics,* Vol.24, No.9, pp. 1385-1394. Cook, I. J.; Dodds, W. J.; Dantas, R. O.; Massey, B.; Kern, M. K.; Lang, I. M.; et al. (1989).

Opening mechanisms of the human upper esophageal sphincter. *American Journal of*

of dysphagia therapy using adjunctive sEMG biofeedback. *Dysphagia,* Vol.19, No.3,

Biofeedback in the treatment of Dysphagia, *American Journal of Speech-Language*

(2002). Videofluoroscopic assessment of patients with dysphagia: pharyngeal retention is a predictive factor for aspiration. *AJR American Journal of Roentgenology,*

effortful and noneffortful swallows in healthy middle- aged and older adults.

Properties of Suprahyoid Muscles and their Potential for Moving the Hyoid.

**5. References**

No.2, pp. 67-72.

No.1, pp. 128-134.

pp. 160-164.

*Pathology,* Vol.9, pp.116-125.

Vol.178, No.2, pp. 393-398.

*Laryngoscope*, Vol.97, No.4, pp. 483-489.

*Dysphagia*, Online First, DOI 10.1007/s00455-010-9315-z.

aspiration scale. *Dysphagia*, Vol.11, No.2, pp. 93-98.

*Dysphagia,* Vol.16, No.3, pp. 190-195.

*Physiology,* Vol.257, No.5, Part 1, pp. G748-759.

secondary to brainstem stroke. *Dysphagia*, Vol.10, pp. 6-18.

to 0.27 seconds for both stimuli. Participant 3 showed an improvement in vallecular residues with thick liquids from moderate to mild, and complete resolution of mild vallecular and severe pyriform sinus residues with thin liquids. Thus, like participant 1, he can be considered to have shown functional improvement related to his practice of the Mendelsohn Maneuver, with increased efficiency of pharyngo-esophageal bolus transfer represented by greater clearance despite shorter and narrower UES opening. Penetration-Aspiration Scale scores were not expected to necessarily change as a result of Mendelsohn Maneuver practice, but did improve from a level 3 to 2 with thin liquids in participant 3.

## **4. Discussion**

These data illustrate the challenges inherent in extracting and interpreting measures of physiological change in swallowing using surface EMG signals and videofluoroscopy recordings. In this series of patients, we chose to measure penetration-aspiration and postswallow residues as our primary functional measures, but also explored more detailed physiological change through measures of hyoid excursion and UES opening.

Several important findings can be extracted from this case series:

These data support the use of sEMG biofeedback to facilitate practice of the effortful swallow and Mendelsohn maneuver techniques in select patients with dysphagia, based on careful analysis of baseline videofluoroscopic swallowing studies showing evidence of the target physiological deficits. We caution that post-treatment outcome measurements require a repeat videofluoroscopic swallowing examination, to ensure that function has improved. Interpretations of swallowing function should not be made on the basis of sEMG signals alone.

#### **5. References**

326 Applications of EMG in Clinical and Sports Medicine

to 0.27 seconds for both stimuli. Participant 3 showed an improvement in vallecular residues with thick liquids from moderate to mild, and complete resolution of mild vallecular and severe pyriform sinus residues with thin liquids. Thus, like participant 1, he can be considered to have shown functional improvement related to his practice of the Mendelsohn Maneuver, with increased efficiency of pharyngo-esophageal bolus transfer represented by greater clearance despite shorter and narrower UES opening. Penetration-Aspiration Scale scores were not expected to necessarily change as a result of Mendelsohn Maneuver

These data illustrate the challenges inherent in extracting and interpreting measures of physiological change in swallowing using surface EMG signals and videofluoroscopy recordings. In this series of patients, we chose to measure penetration-aspiration and postswallow residues as our primary functional measures, but also explored more detailed

1. Surface EMG amplitudes should not be compared across individuals, due to

2. In order to track changes in sEMG amplitudes across time within an individual, care must be taken to position the recording electrodes in the same location at each session, and a data normalization procedure should be used to transform amplitudes relative to a reference task. Additionally, an adequate number of data points is needed to support single-subject monitoring for change across sessions using

3. Change in effortful saliva swallow performance will vary across individuals, perhaps as

4. Effortful saliva swallow practice appears to contribute to improvements in swallowing safety, as measured by the Penetration-Aspiration Scale (Rosenbek et al., 1996), although this interpretation should be treated with caution given that these were not compared to data from controls. Post-swallow residues appear to remain unchanged

5. Change in Mendelsohn maneuver performance may vary across individuals, with

6. Mendelsohn maneuver practice appears to influence hyoid excursion during swallowing, as well as swallowing efficiency, reflected by the degree of postswallow residue achieved, despite narrower diameters and shorter durations of

These data support the use of sEMG biofeedback to facilitate practice of the effortful swallow and Mendelsohn maneuver techniques in select patients with dysphagia, based on careful analysis of baseline videofluoroscopic swallowing studies showing evidence of the target physiological deficits. We caution that post-treatment outcome measurements require a repeat videofluoroscopic swallowing examination, to ensure that function has improved. Interpretations of swallowing function should not be made on the basis of sEMG signals

change either in durational aspects or amplitude aspects of the maneuver.

practice, but did improve from a level 3 to 2 with thin liquids in participant 3.

physiological change through measures of hyoid excursion and UES opening.

fluctuations in amplitude attributable to tissue composition differences.

a function of initial strength, task load and treatment intensity.

Several important findings can be extracted from this case series:

standard deviation banding methods.

after practice of the effortful saliva swallow.

UES opening.

alone.

**4. Discussion**

**20**

*UK*

**Relating Surface Electromyograms of the Facial**

Surface Electromyography (sEMG) has been widely used for studying human mastication, due to its ability to non-invasively monitor the activity of muscle groups associated with chewing (Boyar & Kilcast, 1986; Brown, 1994). It is regarded as the "gold standard" for objective measurement of simple parameters relating to chewing behaviour, such as chew rate and strength. Attempts have been made to approach a quantitative analysis of the sEMG signals, in terms of mechanical action on foods (Agrawal et al., 1998; Braxton et al., 1996; Brown et al., 1996, 1998; Horio & Kawamura, 1989; Mioche & Martin, 1998; Wilson & Brown, 1997; Yven et al., 2010). sEMG has been used to identify differences in chewing patterns between individuals, and to classify them into groups according to their chewing

There is also interest in using sEMG data in studies of sensory texture (Duizer et al., 1996; Lee et al., 2009; Mathoniere et al., 2000; Mioche, 2004; Peng et al., 2002; Sun et al., 2001; Yven et al., 2010). Thus sEMG of the masticatory muscles provides a potential link between physical or mechanical properties of foodstuffs, and sensory evaluations, in particular of

In this chapter we present the hardware and software needed to conduct sEMG of facial muscles during mastication, and describe the signal that is acquired, as well as options for further data treatment of this signal to obtain quantities that can be analysed by statistical means (Section 1). We highlight some of the issues that may be encountered downstream, in particular arising from the variability associated with the technique, especially betweenvolunteer and between-session variance (Section 2). We show that with judicious data scaling (Section 2.4), sEMG can be linked to mechanical and sensory properties of food (Section 3). Throughout, we use examples of our own data, collected during a series of

To obtain sEMG signals from the masticatory muscles, disposable surface electrodes are attached in pairs to the surface of the skin above the masseter and temporal muscle groups, on both sides of the face (figure 1). The muscular action potentials (MUAPs) associated with chewing actions (the sum of which effectively constitutes the signal that is recorded) are

efficiency (Blissett et al., 2006; González et al., 2002; Lassauzay et al., 2000).

related projects carried out at the Institute of Food Research in Norwich.

**1.1 Collecting sEMG signals from mastication**

**1. Introduction**

texture, made by consumers.

**Muscles During Mastication to the Mechanical**

**and Sensory Properties of Foodstuffs**

E. Katherine Kemsley and Marianne Defernez

*Institute of Food Research*

Steele, C. M.; Bailey, G. L.; Chau, T.; Molfenter, S. M.; Oshalla, M.; Waito, A. A. & Zoratto, D.C.B.H. (2011). The relationship between hyoid and laryngeal displacement and swallowing impairment. *Clinical Otolaryngology*, Vol.36, pp.30-36.

## **Relating Surface Electromyograms of the Facial Muscles During Mastication to the Mechanical and Sensory Properties of Foodstuffs**

E. Katherine Kemsley and Marianne Defernez *Institute of Food Research UK*

#### **1. Introduction**

328 Applications of EMG in Clinical and Sports Medicine

Steele, C. M.; Bailey, G. L.; Chau, T.; Molfenter, S. M.; Oshalla, M.; Waito, A. A. & Zoratto,

swallowing impairment. *Clinical Otolaryngology*, Vol.36, pp.30-36.

D.C.B.H. (2011). The relationship between hyoid and laryngeal displacement and

Surface Electromyography (sEMG) has been widely used for studying human mastication, due to its ability to non-invasively monitor the activity of muscle groups associated with chewing (Boyar & Kilcast, 1986; Brown, 1994). It is regarded as the "gold standard" for objective measurement of simple parameters relating to chewing behaviour, such as chew rate and strength. Attempts have been made to approach a quantitative analysis of the sEMG signals, in terms of mechanical action on foods (Agrawal et al., 1998; Braxton et al., 1996; Brown et al., 1996, 1998; Horio & Kawamura, 1989; Mioche & Martin, 1998; Wilson & Brown, 1997; Yven et al., 2010). sEMG has been used to identify differences in chewing patterns between individuals, and to classify them into groups according to their chewing efficiency (Blissett et al., 2006; González et al., 2002; Lassauzay et al., 2000).

There is also interest in using sEMG data in studies of sensory texture (Duizer et al., 1996; Lee et al., 2009; Mathoniere et al., 2000; Mioche, 2004; Peng et al., 2002; Sun et al., 2001; Yven et al., 2010). Thus sEMG of the masticatory muscles provides a potential link between physical or mechanical properties of foodstuffs, and sensory evaluations, in particular of texture, made by consumers.

In this chapter we present the hardware and software needed to conduct sEMG of facial muscles during mastication, and describe the signal that is acquired, as well as options for further data treatment of this signal to obtain quantities that can be analysed by statistical means (Section 1). We highlight some of the issues that may be encountered downstream, in particular arising from the variability associated with the technique, especially betweenvolunteer and between-session variance (Section 2). We show that with judicious data scaling (Section 2.4), sEMG can be linked to mechanical and sensory properties of food (Section 3). Throughout, we use examples of our own data, collected during a series of related projects carried out at the Institute of Food Research in Norwich.

#### **1.1 Collecting sEMG signals from mastication**

To obtain sEMG signals from the masticatory muscles, disposable surface electrodes are attached in pairs to the surface of the skin above the masseter and temporal muscle groups, on both sides of the face (figure 1). The muscular action potentials (MUAPs) associated with chewing actions (the sum of which effectively constitutes the signal that is recorded) are

Relating Surface Electromyograms of the Facial Muscles

During Mastication to the Mechanical and Sensory Properties of Foodstuffs 331

Fig. 2. Data from the right temporalis and right masseter channels from a volunteer consuming one bite of apple. The main features are marked. Note the equivalent vertical scales – in this example, the temporalis muscle group produced slightly larger signals, although there is clearly substantive involvement of both muscle groups. This is typical of

work has adopted this kind of approach, introduced in the following section.

Depending on the food being consumed and the purpose of the study, sEMG readings may be collected over periods of up to several minutes. Consequently the amount of raw data produced can be very large. Until relatively recently, complete raw measurements have been too difficult to handle on standard desktop computers. Researchers have of necessity analysed only simplified forms of this data, such as the chew interval and rate. Figure 3 illustrates the principle by which these values are extracted from the sEMG signals. The reduction in complexity and size of the dataset is substantial: from 80,000 data points in the raw signals shown here, to a few tens of extracted values and summary statistics. Much of the literature examines how these extracted quantities relate to the properties of the food being consumed (Blisset et al., 2006; Braxton et al., 1996; Brown, 1994; Brown & Braxton, 2000; Brown et al., 1996, 1998; Duizer et al., 1996; Lee et al., 2009; Plesh et al., 1986; Tornberg et al., 1985; Veyrune & Mioche, 2000; Yven et al., 2010). However, as the power of computer processors continues to increase, so does the capacity to handle larger amounts of data, and more recently, researchers have begun to utilise the whole signal. The majority of our sEMG

normal chewing behaviour.

small and require amplification, typically using a polygraph. The outputs from the amplifier are passed to an analogue-to-digital converter (ADC), generally operating at a sampling rate of 1kHz, and captured by a PC. The resulting signal from each electrode is a time-varying response representing each muscle group's bioelectrical activity for the duration of the chewing episode.

Fig. 1. Diagram showing a volunteer ready for sEMG recording of mastication. It is useful to take photographs of each volunteer once they are wired up, to assist in reducing intersession variability.

The data presented throughout this chapter were collected using equipment as follows: bipolar disposable electrodes (Blue Sensor A-50-VS, Medicotest, Ølstykke, Denmark); a polygraph amplifier (model 7E, Grass Instrument Co., Massachusetts, USA); and an ADC interface (model 1401plus, Cambridge Electronic Design Ltd., Cambridge, UK). Data were logged by connecting the ADC to a desktop personal computer running the Spike2 data collection software package (Cambridge Electronic Design Ltd., Cambridge, UK). All subsequent signal processing was carried out in Matlab (The Mathworks, Cambridge, UK) installed with the Statistics Toolbox.

#### **1.2 Time-domain sEMG signals – the electromyograms**

Typical signals from two electrode channels (right temporalis and right masseter) obtained from a volunteer consuming one bite of apple are shown in figure 2. The main features are indicated. The first bite and subsequent chews appear as large, regularly spaced features, and these arise from the gross movements of the masticatory muscles. The end of chewing is marked by a swallow, which can be seen fairly clearly in these particular sEMG signals. However, swallow movements are not always distinctive (Hodgson et al., 2003), and we have on occasion found it useful to provide a "swallow indicator" device to be operated by the volunteer, so that the moment of swallowing is precisely located in time (Sprunt et al., 2002). Note the correspondence between the activities of the temporalis and masseter muscle groups. For normal, healthy chewing behaviour, there is involvement of both these muscle groups to similar extents. Likewise, a degree of correspondence is found between the right and left electrode channels.

small and require amplification, typically using a polygraph. The outputs from the amplifier are passed to an analogue-to-digital converter (ADC), generally operating at a sampling rate of 1kHz, and captured by a PC. The resulting signal from each electrode is a time-varying response representing each muscle group's bioelectrical activity for the duration of the

Fig. 1. Diagram showing a volunteer ready for sEMG recording of mastication. It is useful to take photographs of each volunteer once they are wired up, to assist in reducing inter-

The data presented throughout this chapter were collected using equipment as follows: bipolar disposable electrodes (Blue Sensor A-50-VS, Medicotest, Ølstykke, Denmark); a polygraph amplifier (model 7E, Grass Instrument Co., Massachusetts, USA); and an ADC interface (model 1401plus, Cambridge Electronic Design Ltd., Cambridge, UK). Data were logged by connecting the ADC to a desktop personal computer running the Spike2 data collection software package (Cambridge Electronic Design Ltd., Cambridge, UK). All subsequent signal processing was carried out in Matlab (The Mathworks, Cambridge, UK)

Typical signals from two electrode channels (right temporalis and right masseter) obtained from a volunteer consuming one bite of apple are shown in figure 2. The main features are indicated. The first bite and subsequent chews appear as large, regularly spaced features, and these arise from the gross movements of the masticatory muscles. The end of chewing is marked by a swallow, which can be seen fairly clearly in these particular sEMG signals. However, swallow movements are not always distinctive (Hodgson et al., 2003), and we have on occasion found it useful to provide a "swallow indicator" device to be operated by the volunteer, so that the moment of swallowing is precisely located in time (Sprunt et al., 2002). Note the correspondence between the activities of the temporalis and masseter muscle groups. For normal, healthy chewing behaviour, there is involvement of both these muscle groups to similar extents. Likewise, a degree of correspondence is found between the right

chewing episode.

session variability.

installed with the Statistics Toolbox.

and left electrode channels.

**1.2 Time-domain sEMG signals – the electromyograms**

Fig. 2. Data from the right temporalis and right masseter channels from a volunteer consuming one bite of apple. The main features are marked. Note the equivalent vertical scales – in this example, the temporalis muscle group produced slightly larger signals, although there is clearly substantive involvement of both muscle groups. This is typical of normal chewing behaviour.

Depending on the food being consumed and the purpose of the study, sEMG readings may be collected over periods of up to several minutes. Consequently the amount of raw data produced can be very large. Until relatively recently, complete raw measurements have been too difficult to handle on standard desktop computers. Researchers have of necessity analysed only simplified forms of this data, such as the chew interval and rate. Figure 3 illustrates the principle by which these values are extracted from the sEMG signals. The reduction in complexity and size of the dataset is substantial: from 80,000 data points in the raw signals shown here, to a few tens of extracted values and summary statistics. Much of the literature examines how these extracted quantities relate to the properties of the food being consumed (Blisset et al., 2006; Braxton et al., 1996; Brown, 1994; Brown & Braxton, 2000; Brown et al., 1996, 1998; Duizer et al., 1996; Lee et al., 2009; Plesh et al., 1986; Tornberg et al., 1985; Veyrune & Mioche, 2000; Yven et al., 2010). However, as the power of computer processors continues to increase, so does the capacity to handle larger amounts of data, and more recently, researchers have begun to utilise the whole signal. The majority of our sEMG work has adopted this kind of approach, introduced in the following section.

Relating Surface Electromyograms of the Facial Muscles

During Mastication to the Mechanical and Sensory Properties of Foodstuffs 333

of data values (a process called "zero filling" can be used to force a match between readings). All that is required is that the signals were collected at the same sampling rate. A consequence of the inherently small electrical signals measured in sEMG is that the electromyograms, as well as the resulting power spectra, invariably contain unwanted noise arising from the electronics in the amplifier and ADC. It is useful, therefore, to improve the signal-to-noise ratio (SNR) as far as possible through signal averaging or "co-adding" of replicate measurements. Precisely what constitutes a "replicate" is an issue that needs some consideration. For example, we noted above that the electromyograms from the different electrode pairs produce highly correlated signals, and these individual signals could be coadded in the frequency-domain to improve the SNR. Another method of increasing the SNR is to co-add power spectra from successive time windows; overlapping time windows can also be used (in effect, a "moving average" smoothing process). These methods are illustrated in figure 4. The proviso is that the signal-averaging method should be consistent with the aims of the study. For instance, the window method is appropriate if there is no specific requirement to study muscle activity as a function of time; co-adding different electrode channels is

appropriate if there is no desire to study the different muscle groups separately.

Fig. 4. Signal-averaging using windows in the time-domain. The electromyograms on the left side of the figure were collected from one electrode channel, and two different episodes of chewing. The power spectra on the right side of the figure were calculated from the electromyograms using the window method of signal-averaging. In (a), the signal was subdivided into 2-second windows (zero-filling was used to pad the last of these to give a window of the same duration). In (b), a window of 2-seconds width was "moved" along the time axis, in increments of 0.02 seconds, to effect smoothing. In both cases, all windows were Fourier transformed and the resulting spectra co-added to give a single, signal-averaged

Some of the main features of typical sEMG power spectra are also indicated on figure 4. The frequency scale is in Hertz, with its range determined by the ADC sampling rate. Frequency

power spectrum in the frequency-domain.

Fig. 3. Extracting parameters from the raw electromyograms.

#### **1.3 Frequency domain signals – the power spectra**

Electromyograms are collected in the time-domain, as measurements of a response as a function of time. The mathematical procedure of Fourier transformation can be used to express these readings instead as a function of frequency. The fast Fourier transform (FFT) algorithm operates on a digitized time-domain signal, sampled at a rate ν, to produce a frequency-domain spectrum, in which the maximum frequency that can be detected is ν/2 (a consequence of the Nyquist sampling theorem). The square of the amplitude of the Fourier-transformed signal is known as the power spectrum.

Fourier transformation has been applied to sEMG signals in the past, with the mean frequency being the most often used derived parameter as it has been found to relate to muscle fatigue (see review in Al-Mulla et al., 2011) and has also been shown to be linked to the proportions of different fiber types (Yuen et al., 1989). However there may be more subtle, frequency-specific information in the sEMG signals that can be exploited. To access this information, one needs to look separately at bands of frequencies (Schumann et al., 1994) or at the power spectrum itself (Subasi, 2006). Indeed in our own work we have investigated whether different regions of the power spectrum can be related to other data types collected during the experiment. One of the main advantages of working in the frequency domain is that registering (aligning) several electromyograms across different episodes of chewing becomes straightforward. This is because the range of the frequency scale is related solely to the ADC sampling rate. There is no need for the electromyograms to match with regard to the start or the end of recording, nor to even contain the same number

Electromyograms are collected in the time-domain, as measurements of a response as a function of time. The mathematical procedure of Fourier transformation can be used to express these readings instead as a function of frequency. The fast Fourier transform (FFT) algorithm operates on a digitized time-domain signal, sampled at a rate ν, to produce a frequency-domain spectrum, in which the maximum frequency that can be detected is ν/2 (a consequence of the Nyquist sampling theorem). The square of the amplitude of the

Fourier transformation has been applied to sEMG signals in the past, with the mean frequency being the most often used derived parameter as it has been found to relate to muscle fatigue (see review in Al-Mulla et al., 2011) and has also been shown to be linked to the proportions of different fiber types (Yuen et al., 1989). However there may be more subtle, frequency-specific information in the sEMG signals that can be exploited. To access this information, one needs to look separately at bands of frequencies (Schumann et al., 1994) or at the power spectrum itself (Subasi, 2006). Indeed in our own work we have investigated whether different regions of the power spectrum can be related to other data types collected during the experiment. One of the main advantages of working in the frequency domain is that registering (aligning) several electromyograms across different episodes of chewing becomes straightforward. This is because the range of the frequency scale is related solely to the ADC sampling rate. There is no need for the electromyograms to match with regard to the start or the end of recording, nor to even contain the same number

Fig. 3. Extracting parameters from the raw electromyograms.

Fourier-transformed signal is known as the power spectrum.

**1.3 Frequency domain signals – the power spectra**

of data values (a process called "zero filling" can be used to force a match between readings). All that is required is that the signals were collected at the same sampling rate.

A consequence of the inherently small electrical signals measured in sEMG is that the electromyograms, as well as the resulting power spectra, invariably contain unwanted noise arising from the electronics in the amplifier and ADC. It is useful, therefore, to improve the signal-to-noise ratio (SNR) as far as possible through signal averaging or "co-adding" of replicate measurements. Precisely what constitutes a "replicate" is an issue that needs some consideration. For example, we noted above that the electromyograms from the different electrode pairs produce highly correlated signals, and these individual signals could be coadded in the frequency-domain to improve the SNR. Another method of increasing the SNR is to co-add power spectra from successive time windows; overlapping time windows can also be used (in effect, a "moving average" smoothing process). These methods are illustrated in figure 4. The proviso is that the signal-averaging method should be consistent with the aims of the study. For instance, the window method is appropriate if there is no specific requirement to study muscle activity as a function of time; co-adding different electrode channels is appropriate if there is no desire to study the different muscle groups separately.

Fig. 4. Signal-averaging using windows in the time-domain. The electromyograms on the left side of the figure were collected from one electrode channel, and two different episodes of chewing. The power spectra on the right side of the figure were calculated from the electromyograms using the window method of signal-averaging. In (a), the signal was subdivided into 2-second windows (zero-filling was used to pad the last of these to give a window of the same duration). In (b), a window of 2-seconds width was "moved" along the time axis, in increments of 0.02 seconds, to effect smoothing. In both cases, all windows were Fourier transformed and the resulting spectra co-added to give a single, signal-averaged power spectrum in the frequency-domain.

Some of the main features of typical sEMG power spectra are also indicated on figure 4. The frequency scale is in Hertz, with its range determined by the ADC sampling rate. Frequency

Relating Surface Electromyograms of the Facial Muscles

the volunteer-specific characteristics that persist across sessions.

Fig. 5. Different representations of the frequency domain dataset.

from two of the volunteers.

Fig. 6. An expansion of the low-frequency (<10Hz) region in a selection of the power spectra

During Mastication to the Mechanical and Sensory Properties of Foodstuffs 335

volunteer: five episodes of chewing at each of five sessions). Clear blocks of data can be seen, corresponding to the 25 spectra from each individual. The right hand side of the figure shows the mean power spectra, obtained by averaging across all chewing episodes and sessions for each volunteer. Again, there are clear differences between the signals for each individual, although mean spectra alone do not convey any information about the withinvolunteer variation, in contrast to the heatmap view. An expansion of the low-frequency (<10Hz) region in a selection of the power spectra is shown in figure 6, highlighting some of

resolution is determined by the number of data points Fourier transformed (i.e., in each electromyogram window). Features arising from the main muscle movements involved in chewing can be seen in the low-frequency region (v < 10Hz). The broad depression centred at 50Hz arises from a band-stop filter present in the polygraph to suppress interference from the mains electricity supply. In some of the data, overtones of this ν = 50Hz interference can be seen at ν + 2ν, ν + 4ν,… There are also some distinct features in the region 10 – 50Hz. In sEMG studies of a range of different muscle groups, these are generally attributed to synchronized activity of muscle motor units arising from rhythmic firing of the central nervous system (see for example McAuley et al., 1997, and the review of the literature included therein).

As they share a common frequency scale, power spectra can be easily collated into a matrix for processing with linear algebra techniques. This enables comparison across large collections of sEMG data, acquired for example from different volunteers, and/or from the consumption of a range of foodstuffs. This possibility is exploited extensively in different studies that will be described in the remainder of this chapter, all of which involved large collections of sEMG data from multiple volunteers. The data acquisition equipment for all studies was as described above, i.e., electromyograms were collected from four muscle groups using an ADC operating at a 1kHz sampling rate. In all cases, prior approval to conduct the study was obtained from the local ethics committee.

## **2. Between-volunteer differences in sEMG**

#### **2.1 A study of edible gels with characterised textural attributes**

The first of the case studies comprises sEMG signals recorded from eight volunteers, who each consumed five edible gels on five separate occasions. This work formed part of a wider study on flavour release (Kemsley et al., 2002; Smith, 2004; Sprunt et al., 2002; Sprunt & Smith, 2002; Wright & Hills, 2003; Wright et al., 2003). All gels were prepared using the same nominal formulation, and then flavoured with five varying amounts of anethole. Complete details of the gel preparation and serving order can be found in Kemsley et al. (2002). Mechanical properties of the gels were determined using a TA-XT2 Texture Analyser (Stable Micro Systems, Godalming, Surrey, UK). A single-sided razor blade (length 39 mm, depth 12.5 mm, thickness 0.25 mm) was driven vertically through the gel axis at 0.05mm/sec (total travel approximately 12mm), and the work done in cutting calculated from the integrated force-distance curve.

sEMG recording was discontinued in each instance when the volunteer indicated that the gel's flavour could no longer be perceived. The minimum duration of any electromyogram was ~32 seconds. Consequently, the first 32768 (=215) data values in each electromyogram were passed to a fast Fourier transform algorithm and converted to power spectra, each containing 16384 data values. To improve the SNR, these were summed across the four electrode channels to give a single, signal-averaged power spectrum per chewing episode.

#### **2.1.1 Visual examination of the data**

From visual comparison of the sEMG signals from different volunteers, it was found that each volunteer exhibits highly characteristic chewing behaviour, and furthermore, this is broadly consistent between sessions. This is most evident from examination of the power spectra. In figure 5, two different representations of the frequency domain dataset are shown. On the left hand side, the complete matrix of [200 x 16384] data values is shown as a heatmap. Each row in this map represents a single power spectrum, and the data have been ordered such that the spectra from individual volunteers are grouped together (25 per

resolution is determined by the number of data points Fourier transformed (i.e., in each electromyogram window). Features arising from the main muscle movements involved in chewing can be seen in the low-frequency region (v < 10Hz). The broad depression centred at 50Hz arises from a band-stop filter present in the polygraph to suppress interference from the mains electricity supply. In some of the data, overtones of this ν = 50Hz interference can be seen at ν + 2ν, ν + 4ν,… There are also some distinct features in the region 10 – 50Hz. In sEMG studies of a range of different muscle groups, these are generally attributed to synchronized activity of muscle motor units arising from rhythmic firing of the central nervous system (see

As they share a common frequency scale, power spectra can be easily collated into a matrix for processing with linear algebra techniques. This enables comparison across large collections of sEMG data, acquired for example from different volunteers, and/or from the consumption of a range of foodstuffs. This possibility is exploited extensively in different studies that will be described in the remainder of this chapter, all of which involved large collections of sEMG data from multiple volunteers. The data acquisition equipment for all studies was as described above, i.e., electromyograms were collected from four muscle groups using an ADC operating at a 1kHz sampling rate. In all cases, prior approval to

The first of the case studies comprises sEMG signals recorded from eight volunteers, who each consumed five edible gels on five separate occasions. This work formed part of a wider study on flavour release (Kemsley et al., 2002; Smith, 2004; Sprunt et al., 2002; Sprunt & Smith, 2002; Wright & Hills, 2003; Wright et al., 2003). All gels were prepared using the same nominal formulation, and then flavoured with five varying amounts of anethole. Complete details of the gel preparation and serving order can be found in Kemsley et al. (2002). Mechanical properties of the gels were determined using a TA-XT2 Texture Analyser (Stable Micro Systems, Godalming, Surrey, UK). A single-sided razor blade (length 39 mm, depth 12.5 mm, thickness 0.25 mm) was driven vertically through the gel axis at 0.05mm/sec (total travel approximately 12mm), and the work done in cutting calculated

sEMG recording was discontinued in each instance when the volunteer indicated that the gel's flavour could no longer be perceived. The minimum duration of any electromyogram was ~32 seconds. Consequently, the first 32768 (=215) data values in each electromyogram were passed to a fast Fourier transform algorithm and converted to power spectra, each containing 16384 data values. To improve the SNR, these were summed across the four electrode channels to give a single, signal-averaged power spectrum per chewing episode.

From visual comparison of the sEMG signals from different volunteers, it was found that each volunteer exhibits highly characteristic chewing behaviour, and furthermore, this is broadly consistent between sessions. This is most evident from examination of the power spectra. In figure 5, two different representations of the frequency domain dataset are shown. On the left hand side, the complete matrix of [200 x 16384] data values is shown as a heatmap. Each row in this map represents a single power spectrum, and the data have been ordered such that the spectra from individual volunteers are grouped together (25 per

for example McAuley et al., 1997, and the review of the literature included therein).

conduct the study was obtained from the local ethics committee.

**2.1 A study of edible gels with characterised textural attributes**

**2. Between-volunteer differences in sEMG**

from the integrated force-distance curve.

**2.1.1 Visual examination of the data**

volunteer: five episodes of chewing at each of five sessions). Clear blocks of data can be seen, corresponding to the 25 spectra from each individual. The right hand side of the figure shows the mean power spectra, obtained by averaging across all chewing episodes and sessions for each volunteer. Again, there are clear differences between the signals for each individual, although mean spectra alone do not convey any information about the withinvolunteer variation, in contrast to the heatmap view. An expansion of the low-frequency (<10Hz) region in a selection of the power spectra is shown in figure 6, highlighting some of the volunteer-specific characteristics that persist across sessions.

Fig. 5. Different representations of the frequency domain dataset.

Fig. 6. An expansion of the low-frequency (<10Hz) region in a selection of the power spectra from two of the volunteers.

Relating Surface Electromyograms of the Facial Muscles

sEMG data (De Luca, 1997).

(Westbury & Shaughnessy, 1987).

**2.2 Between-volunteer differences in studies of prescribed chewing**

A subsequent multi-volunteer study aimed to further explore the causes of between-volunteer variation. In particular, we sought to examine the extent to which subjectivity in sEMG signals arises from a factor potentially within the conscious control of individuals: the manner in which they chew. For this work, we dispensed with an edible stimulus (and the associated phenomena of particle breakdown and saliva stimulation). There are literature precedents for the use of model stimuli in studies of mechanical properties and sEMG responses. The use of miniature load cells was described by Mioche et al. (1993), who recorded bite forces from 25 to 124N at biting rates of 1 and 2Hz. The force-deformation behaviour of a model material, Optosil, has been compared with that of real foodstuffs by Olthoff et al. (1986). Edlund & Lamm (1980) compared sEMG signals for various foods and Optosil, and Horio & Kawamura (1989) compared sEMG signals with instrumental texture parameters for foods. There is also

**2.1.3 Discussion**

During Mastication to the Mechanical and Sensory Properties of Foodstuffs 337

well as from the remaining volunteers. This is particularly remarkable considering each volunteer's data comprised measurements from five recording sessions, separated in time by periods of days. It also confirms the suggestion from qualitative examination of the spectra that volunteers can be associated with a characteristic low-frequency spectrum. Closer examination of figure 7 showed that within each volunteer's batch of signals, data from the different sessions tended to cluster together also. For clarity, this is indicated for one volunteer's readings only. Inter-session variability is likely to arise largely from irreproducibility in positioning the electrodes. This is an established source of variance in

The data collected in this study were also treated in the time-domain by Wright et al. (2003), who examined the distribution of the intervals between chews, and showed that a different distribution was obtained from each volunteer. Our multivariate treatment of the power spectra has further indicated that each single sEMG recording can be identified as originating from a distinct volunteer. In addition to the readily observed differences in the low-frequency region, there are characteristic differences between individuals' responses at higher spectral frequencies, as seen from the heatmap view. It is known that the shape of the frequency distribution changes in response to fatigue, with a shift in the median towards lower frequencies. This has been shown in both sEMG (see review in Al-Mulla et al., 2011) and vibromyography (VMG) studies (Herzog et al., 1994). However, volunteers will differ in the rate at which they become fatigued, and in the extent to which this is manifested in their sEMG signal. All of these findings suggested that when volunteers are asked to consume a foodstuff using their natural chewing behaviour, they do so in an individual, characteristic fashion. Consistent differences between volunteers' sEMG characteristics could also be due to a variety of behavioural and anatomical factors. Individuals differ in terms of their efficiency in breaking down the food bolus, which is influenced by salivary flow rates and the ability to reform and move the bolus in the mouth. In addition, there is variation between individuals in their pattern of muscle activation, and hence in terms of the level of muscle recruitment and activation in the chewing sequence. This arises to some extent from the anatomy of the masticatory apparatus, as well as characteristics such as the muscle fibretype construction (proportion of type I and II fibres), the fibre size and motor unit arrangement, the thickness of the subcutaneous layer and the electrical resistance of the skin

#### **2.1.2 Multivariate analysis - PCA**

An alternative and highly efficient means of viewing a large data matrix of this kind is to process it with a data compression technique such as principal component analysis (PCA) (Jolliffe, 1986; Krzanowshi, 1988). This method produces a transformed matrix - the PC scores which contains most of the information that was present in the original data matrix, but in a rearranged form. Crucially, the scores matrix is much smaller in size, making it considerably easier to examine graphically. Furthermore, the scores are ranked by their variances, thus the largest (and hopefully most interesting) sources of variation are represented in the first few scores, whilst the noise component is relegated to lower scores. The PCA transformation can thus lead to an effective increase in the SNR of the systematic information of interest.

Figure 7 shows the first two PC scores obtained from the set of power spectra, using correlation-method PCA. Together, these two scores alone account for over 25% of the information content (variance) that was present in the original data set. Each point on this plot represents a power spectrum from one episode of chewing. It can be immediately seen that there is considerable clustering of the data arising from each individual. Data from volunteers 1, 2 and 6, for example, can be completely distinguished from one another, as

Fig. 7. PCA (correlation method) applied to sEMG power spectra from edible gels. The entire frequency range (0 – 500Hz) was used. Sessions for one volunteer (2) are marked by a number (1-5)

well as from the remaining volunteers. This is particularly remarkable considering each volunteer's data comprised measurements from five recording sessions, separated in time by periods of days. It also confirms the suggestion from qualitative examination of the spectra that volunteers can be associated with a characteristic low-frequency spectrum. Closer examination of figure 7 showed that within each volunteer's batch of signals, data from the different sessions tended to cluster together also. For clarity, this is indicated for one volunteer's readings only. Inter-session variability is likely to arise largely from irreproducibility in positioning the electrodes. This is an established source of variance in sEMG data (De Luca, 1997).

#### **2.1.3 Discussion**

336 Applications of EMG in Clinical and Sports Medicine

An alternative and highly efficient means of viewing a large data matrix of this kind is to process it with a data compression technique such as principal component analysis (PCA) (Jolliffe, 1986; Krzanowshi, 1988). This method produces a transformed matrix - the PC scores which contains most of the information that was present in the original data matrix, but in a rearranged form. Crucially, the scores matrix is much smaller in size, making it considerably easier to examine graphically. Furthermore, the scores are ranked by their variances, thus the largest (and hopefully most interesting) sources of variation are represented in the first few scores, whilst the noise component is relegated to lower scores. The PCA transformation can

Figure 7 shows the first two PC scores obtained from the set of power spectra, using correlation-method PCA. Together, these two scores alone account for over 25% of the information content (variance) that was present in the original data set. Each point on this plot represents a power spectrum from one episode of chewing. It can be immediately seen that there is considerable clustering of the data arising from each individual. Data from volunteers 1, 2 and 6, for example, can be completely distinguished from one another, as

thus lead to an effective increase in the SNR of the systematic information of interest.

Fig. 7. PCA (correlation method) applied to sEMG power spectra from edible gels. The entire frequency range (0 – 500Hz) was used. Sessions for one volunteer (2) are marked by a

**2.1.2 Multivariate analysis - PCA**

number (1-5)

The data collected in this study were also treated in the time-domain by Wright et al. (2003), who examined the distribution of the intervals between chews, and showed that a different distribution was obtained from each volunteer. Our multivariate treatment of the power spectra has further indicated that each single sEMG recording can be identified as originating from a distinct volunteer. In addition to the readily observed differences in the low-frequency region, there are characteristic differences between individuals' responses at higher spectral frequencies, as seen from the heatmap view. It is known that the shape of the frequency distribution changes in response to fatigue, with a shift in the median towards lower frequencies. This has been shown in both sEMG (see review in Al-Mulla et al., 2011) and vibromyography (VMG) studies (Herzog et al., 1994). However, volunteers will differ in the rate at which they become fatigued, and in the extent to which this is manifested in their sEMG signal. All of these findings suggested that when volunteers are asked to consume a foodstuff using their natural chewing behaviour, they do so in an individual, characteristic fashion.

Consistent differences between volunteers' sEMG characteristics could also be due to a variety of behavioural and anatomical factors. Individuals differ in terms of their efficiency in breaking down the food bolus, which is influenced by salivary flow rates and the ability to reform and move the bolus in the mouth. In addition, there is variation between individuals in their pattern of muscle activation, and hence in terms of the level of muscle recruitment and activation in the chewing sequence. This arises to some extent from the anatomy of the masticatory apparatus, as well as characteristics such as the muscle fibretype construction (proportion of type I and II fibres), the fibre size and motor unit arrangement, the thickness of the subcutaneous layer and the electrical resistance of the skin (Westbury & Shaughnessy, 1987).

#### **2.2 Between-volunteer differences in studies of prescribed chewing**

A subsequent multi-volunteer study aimed to further explore the causes of between-volunteer variation. In particular, we sought to examine the extent to which subjectivity in sEMG signals arises from a factor potentially within the conscious control of individuals: the manner in which they chew. For this work, we dispensed with an edible stimulus (and the associated phenomena of particle breakdown and saliva stimulation). There are literature precedents for the use of model stimuli in studies of mechanical properties and sEMG responses. The use of miniature load cells was described by Mioche et al. (1993), who recorded bite forces from 25 to 124N at biting rates of 1 and 2Hz. The force-deformation behaviour of a model material, Optosil, has been compared with that of real foodstuffs by Olthoff et al. (1986). Edlund & Lamm (1980) compared sEMG signals for various foods and Optosil, and Horio & Kawamura (1989) compared sEMG signals with instrumental texture parameters for foods. There is also

Relating Surface Electromyograms of the Facial Muscles

individual (figure 9(c)).

low-frequency region of the sEMG spectra.

be regarded as an undesirable phenomenon.

rhythms observed in the studies on gels (compare with figure 6).

**2.2.2 Multivariate analysis: Focussing on different frequency ranges**

During Mastication to the Mechanical and Sensory Properties of Foodstuffs 339

directly to the prescribed chew rates. For most volunteers, this is especially clear for the chew rates of 90bpm and 120bpm. In general, volunteers appeared better able to coordinate and maintain these higher prescribed rates. These were closest to the natural chewing

In theory, this low frequency region is dominated by the features attributable to prescribed chewing only, and since the prescriptions were the same for all individuals in the study, it might be expected that this would reduce the degree to which their sEMG signals can be differentiated. An analysis of the chew interval data, analogous to that carried out by Wright et al. (2003) and reported in our paper (Kemsley et al., 2003) appears to indicate that this is so. From analysis of chew interval distributions, volunteers seemed to differ in their ability to precisely coordinate and maintain the prescribed chewing patterns, but individuals could not be identified by their chew interval readings alone. However, application of PCA to the power spectra, which is able to decompose different sources of systematic variation, shows that there is still some differentiation between volunteers. Figure 9(a) shows the first two PCs obtained from the low frequency (<10Hz) region, using the same method of analysis (correlation PCA) as in figure 7. The prescribed chew rates are indicated for one of the volunteer's data only, for reasons of clarity, along with a "direction" in this PC space that corresponds to variation in the chew rate as marked. It is apparent that there is still some separation of the signals by volunteer, even in this low-frequency region alone. Figure 9(b) shows the results of applying the same PCA method to the >10Hz region. The clustering according to volunteer is more marked. A further PCA analysis was applied to the whole frequency range (i.e. a direct analogue of figure 7); this showed even clearer clustering according to

From examination of these figures, it would seem that variation between volunteers is an effect comparable in magnitude to the grossly different prescribed chew rates. The third and subsequent scores in all the PCA analyses (not shown) indicated some variances associated with chew rate, but smaller than those of PCs 1 and 2. This result is perhaps counterintuitive: one might have anticipated that, when considering the dataset as a whole, by far the largest source of variance would be chew rate. This was, after all, a prescribed difference between conditions for obtaining the readings, the effects of which are clearly visible in the

Next, PCA was applied to the whole frequency range from each chew rate separately. The first two scores from the 120bpm dataset are shown in figure 10. There is now clear clustering and separation of the data from each volunteer. Similar results were obtained from analyses of the 90, 60 and 30bpm sets. This confirms that higher frequencies contain useful information for distinguishing volunteers, and moreover, the volunteer-specific nature of these higher frequencies is not removed by asking volunteers to constrain their chewing behaviour. In agreement with the gel study, our findings once more show that if sEMG signals are considered as a whole, then individual volunteers produce highly characteristic signals, which remain characteristic even when the manner in which they chew is highly controlled. In the context of examining properties of food materials, this must

precedent for the use of a metronome; Plesh et al. (1987) used sEMG and a Kinesiograph to study the jaw movements of subjects chewing gum, at individuals' preferred rates, and also at rates prescribed using a metronome.

In our study, volunteers simulated chewing by elastically deforming calibrated, springloaded wooden pegs held between their incisors, 5mm from the end of the peg arms. The volunteers were asked to coordinate their movements with the sound produced by a metronome, which was operated at several different, predetermined rates: 30, 60, 90 and 120 bpm (beats per minute). Each of six volunteers provided duplicate measurements at the four different rates, on three separate occasions. Mechanical characterisation of the pegs was carried out by compression in a universal test machine (Stable Micro Systems Texture Analyzer, model TA-XT2). The average work done in operating the pegs was 106 mJ measured at a compression point 5mm from the ends of the peg arms.

The raw electromyograms were collected, Fourier transformed and signal-averaged in the same way as in the work on edible gels described above, although in this instance only 30 seconds of data were retained from each reading, as some volunteers indicated fatigue after this time. In all cases, the sEMG responses were greatest on the masseter-right and masseterleft channels. This is because the use of the incisor teeth involves less activity of the temporal and more of the masseter muscles, compared to the study of freestyle chewing of gels, in which more of the facial muscles tended to be involved.

#### **2.2.1 Visual examination of the low-frequency data**

The low-frequency region in a selection of the power spectra are shown in figure 8, which typify the data set as a whole. Sharp peaks can be seen in the power spectra that correspond

Fig. 8. Low-frequency region in spectra from prescribed chewing. For a chew rate of 120bpm, a peak is clearly seen at 2Hz, with some smaller 'overtones' at 4Hz, 6Hz,... When the chew rate is 90bpm, there are peaks at 1.5Hz with overtones at 3Hz, 4.5Hz,..., and when it is 60bpm the peaks occur at 1Hz, with overtones at 2Hz, 3Hz,... In general, volunteers found it harder to precisely coordinate their jaw movements with the lower prescribed rates. Note that no peaks are clearly seen in any volunteers' data from the chew rate of 30bpm.

precedent for the use of a metronome; Plesh et al. (1987) used sEMG and a Kinesiograph to study the jaw movements of subjects chewing gum, at individuals' preferred rates, and also at

In our study, volunteers simulated chewing by elastically deforming calibrated, springloaded wooden pegs held between their incisors, 5mm from the end of the peg arms. The volunteers were asked to coordinate their movements with the sound produced by a metronome, which was operated at several different, predetermined rates: 30, 60, 90 and 120 bpm (beats per minute). Each of six volunteers provided duplicate measurements at the four different rates, on three separate occasions. Mechanical characterisation of the pegs was carried out by compression in a universal test machine (Stable Micro Systems Texture Analyzer, model TA-XT2). The average work done in operating the pegs was 106 mJ

The raw electromyograms were collected, Fourier transformed and signal-averaged in the same way as in the work on edible gels described above, although in this instance only 30 seconds of data were retained from each reading, as some volunteers indicated fatigue after this time. In all cases, the sEMG responses were greatest on the masseter-right and masseterleft channels. This is because the use of the incisor teeth involves less activity of the temporal and more of the masseter muscles, compared to the study of freestyle chewing of gels, in

The low-frequency region in a selection of the power spectra are shown in figure 8, which typify the data set as a whole. Sharp peaks can be seen in the power spectra that correspond

Fig. 8. Low-frequency region in spectra from prescribed chewing. For a chew rate of 120bpm, a peak is clearly seen at 2Hz, with some smaller 'overtones' at 4Hz, 6Hz,... When the chew rate is 90bpm, there are peaks at 1.5Hz with overtones at 3Hz, 4.5Hz,..., and when it is 60bpm the peaks occur at 1Hz, with overtones at 2Hz, 3Hz,... In general, volunteers found it harder to precisely coordinate their jaw movements with the lower prescribed rates. Note that no peaks are clearly seen in any volunteers' data from the chew rate of 30bpm.

measured at a compression point 5mm from the ends of the peg arms.

which more of the facial muscles tended to be involved.

**2.2.1 Visual examination of the low-frequency data**

rates prescribed using a metronome.

directly to the prescribed chew rates. For most volunteers, this is especially clear for the chew rates of 90bpm and 120bpm. In general, volunteers appeared better able to coordinate and maintain these higher prescribed rates. These were closest to the natural chewing rhythms observed in the studies on gels (compare with figure 6).

#### **2.2.2 Multivariate analysis: Focussing on different frequency ranges**

In theory, this low frequency region is dominated by the features attributable to prescribed chewing only, and since the prescriptions were the same for all individuals in the study, it might be expected that this would reduce the degree to which their sEMG signals can be differentiated. An analysis of the chew interval data, analogous to that carried out by Wright et al. (2003) and reported in our paper (Kemsley et al., 2003) appears to indicate that this is so. From analysis of chew interval distributions, volunteers seemed to differ in their ability to precisely coordinate and maintain the prescribed chewing patterns, but individuals could not be identified by their chew interval readings alone. However, application of PCA to the power spectra, which is able to decompose different sources of systematic variation, shows that there is still some differentiation between volunteers. Figure 9(a) shows the first two PCs obtained from the low frequency (<10Hz) region, using the same method of analysis (correlation PCA) as in figure 7. The prescribed chew rates are indicated for one of the volunteer's data only, for reasons of clarity, along with a "direction" in this PC space that corresponds to variation in the chew rate as marked. It is apparent that there is still some separation of the signals by volunteer, even in this low-frequency region alone. Figure 9(b) shows the results of applying the same PCA method to the >10Hz region. The clustering according to volunteer is more marked. A further PCA analysis was applied to the whole frequency range (i.e. a direct analogue of figure 7); this showed even clearer clustering according to individual (figure 9(c)).

From examination of these figures, it would seem that variation between volunteers is an effect comparable in magnitude to the grossly different prescribed chew rates. The third and subsequent scores in all the PCA analyses (not shown) indicated some variances associated with chew rate, but smaller than those of PCs 1 and 2. This result is perhaps counterintuitive: one might have anticipated that, when considering the dataset as a whole, by far the largest source of variance would be chew rate. This was, after all, a prescribed difference between conditions for obtaining the readings, the effects of which are clearly visible in the low-frequency region of the sEMG spectra.

Next, PCA was applied to the whole frequency range from each chew rate separately. The first two scores from the 120bpm dataset are shown in figure 10. There is now clear clustering and separation of the data from each volunteer. Similar results were obtained from analyses of the 90, 60 and 30bpm sets. This confirms that higher frequencies contain useful information for distinguishing volunteers, and moreover, the volunteer-specific nature of these higher frequencies is not removed by asking volunteers to constrain their chewing behaviour. In agreement with the gel study, our findings once more show that if sEMG signals are considered as a whole, then individual volunteers produce highly characteristic signals, which remain characteristic even when the manner in which they chew is highly controlled. In the context of examining properties of food materials, this must be regarded as an undesirable phenomenon.

Relating Surface Electromyograms of the Facial Muscles

During Mastication to the Mechanical and Sensory Properties of Foodstuffs 341

Fig. 10. PCA scores plot of first vs. second PC from the subset of data from the 120bpm chew rate only (using the whole power spectrum frequency range). Data were additionally normalized by setting the integrated spectral area to unity, before correlation-method PCA.

These studies have clearly established that individuals exhibit highly characteristic sEMG spectra, and the relative between-volunteer and within-volunteer variances are such that individuals can be uniquely identified by their sEMG signature. Investigations using subsets of different regions of the frequency range showed that this characteristic nature is maintained even at frequencies higher than those associated with the gross muscle movement. The clustering according to volunteer was found irrespective of whether individuals use natural freestyle chewing behaviour, or were asked to chew in an artificial,

This corroborates the findings from a number of other studies. For example, in a study involving five different foods, Horio & Kawamura (1989) found volunteers to differ in the way the food hardness affected their number of chewing strokes, and overall chewing time. Brown (1994) and Brown et al. (1998) observed significant between-volunteer variation for several temporal and amplitude parameters calculated from the time-domain signal (duration of the

**2.3 Summary: The extent of between-volunteer variance**

highly prescribed manner.

Fig. 9. PCA scores plots for different frequency ranges. In (a), one volunteer's data points are marked with the prescribed chew rate. The arrow indicates the direction in the PC space associated with the chew rate; all volunteers' data demonstrated the same directional trend. There is still noticeable grouping according to volunteer, despite the prescribed chewing behaviour. In figures (b) and (c), different regions of the power spectrum were passed to the PCA procedure; the clustering according to volunteer becomes clearer when frequencies above 10Hz are included.

Fig. 9. PCA scores plots for different frequency ranges. In (a), one volunteer's data points are marked with the prescribed chew rate. The arrow indicates the direction in the PC space associated with the chew rate; all volunteers' data demonstrated the same directional trend. There is still noticeable grouping according to volunteer, despite the prescribed chewing behaviour. In figures (b) and (c), different regions of the power spectrum were passed to the PCA procedure; the clustering according to volunteer becomes clearer when frequencies

above 10Hz are included.

Fig. 10. PCA scores plot of first vs. second PC from the subset of data from the 120bpm chew rate only (using the whole power spectrum frequency range). Data were additionally normalized by setting the integrated spectral area to unity, before correlation-method PCA.

#### **2.3 Summary: The extent of between-volunteer variance**

These studies have clearly established that individuals exhibit highly characteristic sEMG spectra, and the relative between-volunteer and within-volunteer variances are such that individuals can be uniquely identified by their sEMG signature. Investigations using subsets of different regions of the frequency range showed that this characteristic nature is maintained even at frequencies higher than those associated with the gross muscle movement. The clustering according to volunteer was found irrespective of whether individuals use natural freestyle chewing behaviour, or were asked to chew in an artificial, highly prescribed manner.

This corroborates the findings from a number of other studies. For example, in a study involving five different foods, Horio & Kawamura (1989) found volunteers to differ in the way the food hardness affected their number of chewing strokes, and overall chewing time. Brown (1994) and Brown et al. (1998) observed significant between-volunteer variation for several temporal and amplitude parameters calculated from the time-domain signal (duration of the

Relating Surface Electromyograms of the Facial Muscles

(figure 12).

properties of apples.

During Mastication to the Mechanical and Sensory Properties of Foodstuffs 343

cutting. This is not easy to discern in the heavily populated scores plot (figure 11(b)), but much clearer when the score is plotted individually against the textural property separately

This amounts to strong evidence of a within-volunteer trend that associates sEMG reponse with increasing work done in cutting. We can conclude that a statistically significant association exists between the power spectra and the texture as expressed by mechanical cutting. This is an indication that quite subtle properties of foodstuffs have a discernible influence on sEMG readings; this is explored further in the subsequent section which examines data collected from a large-scale study of both the mechanical and sensory

Fig. 11. PCA scores plots of "batchwise" standardized (per volunteer, per session) data set. In (a), the symbols are coded by volunteer; in (b), by edible gel. The numbers in the legend

refer to the mean work done (Nmm) in cutting each of the five preparations.

Fig. 12. Plot of gel texture (work done in cutting) versus the first PC score. Error bars

represent 2 standard deviations.

activity burst, period between bursts of activity, duration of the chewing period, mean voltage within activity burst, maximum voltage, and area under sEMG trace); these were recorded whilst chewing a range of different food types. Volunteers have also been classified into "chewing efficiency" groups, based on the weight loss from a stick of chewing gum, and the median particle size of almonds chewed a specified number of times (Brown & Braxton, 2000, Braxton et al., 1996); when consuming other food types (meat, biscuits), these groups exhibited different behaviours (numbers of chews, total chewing time and chew work). Similarly in a study on confectionery chews, Blisset et al. (1996) characterised groups on the basis of chewing force, chewing rate, proportion of work and total number of chews, and observed that this could influence flavour release, though this was product-dependant. Mioche & Martin (1998) observed clear differences between naïve and trained volunteers, based on their sEMG burst times, chew time, duration and work, in particular in the extent to which their behaviour was modified, comparing chewing with, and without, simultaneous sensory judgment. González et al. (2002) also found that the chewing behaviour of experts (trained subjects) was influenced by the goal of the experiment more than that of untrained subjects; in addition there were differences in chewing time, chewing work and chewing rate between experts and untrained subjects, and a larger between-session variation in untrained subjects for number of chews, chewing time, mean voltage, and chewing work.

In none of these studies was it shown, however, that volunteers exhibit consistent, individually characteristic behaviour over extended periods of time. This finding is important, because it demonstrates the inherent subjectivity of sEMG as a form of measurement. This may need to be mitigated in order to explore underlying, subtle relationships between sEMG readings and material or sensory properties of the stimulus. This is an area of work that will be examined in Section 2.4 and exploited in the remainder of this chapter. Moreover, the higher frequency components of the signal are neglected in conventional analysis of sEMG data, which processes raw signals to obtain parameters such as chew intervals, chew rate, and so on. Our findings show, however,, the high frequency region clearly contains non-trivial information, since volunteers can be distinguished by their high-frequency sEMG spectra alone.

#### **2.4 Data scaling to remove inter-subject and inter-session variance**

We return now to discuss the power spectra collected from the consumption of edible gels. Figure 7 showed clearly the between-session and between-volunteer variances. However, coding the symbols by the texture attributes of the gels revealed no conclusive clustering according to the textural properties (not shown); equally unfruitful was an exhaustive search of subsequent PC dimensions. However, this could simply be because any association with the textural properties is small and not clearly seen against the background of much larger volunteer and session effects.

A straightforward means of removing variation associated with both session and volunteer is to apply some form of scaling to the batches of signals from each volunteer and session. There are many different candidate scaling schemes. Here we use standardization, a wellknown statistical technique which scales each variate (data values at each frequency in the power spectrum) so that they have mean of zero and a standard deviation of unity. When PCA is applied to the "batchwise" standardized data set, we can see from the plot of scores that variation associated with either the volunteer or session is removed (figure 11(a)). However, when we code the symbols by the different gels consumed, a new trend emerges – an association in the first PC dimension with the gel's texture as measured by work done in

activity burst, period between bursts of activity, duration of the chewing period, mean voltage within activity burst, maximum voltage, and area under sEMG trace); these were recorded whilst chewing a range of different food types. Volunteers have also been classified into "chewing efficiency" groups, based on the weight loss from a stick of chewing gum, and the median particle size of almonds chewed a specified number of times (Brown & Braxton, 2000, Braxton et al., 1996); when consuming other food types (meat, biscuits), these groups exhibited different behaviours (numbers of chews, total chewing time and chew work). Similarly in a study on confectionery chews, Blisset et al. (1996) characterised groups on the basis of chewing force, chewing rate, proportion of work and total number of chews, and observed that this could influence flavour release, though this was product-dependant. Mioche & Martin (1998) observed clear differences between naïve and trained volunteers, based on their sEMG burst times, chew time, duration and work, in particular in the extent to which their behaviour was modified, comparing chewing with, and without, simultaneous sensory judgment. González et al. (2002) also found that the chewing behaviour of experts (trained subjects) was influenced by the goal of the experiment more than that of untrained subjects; in addition there were differences in chewing time, chewing work and chewing rate between experts and untrained subjects, and a larger between-session variation in untrained subjects for number of chews,

In none of these studies was it shown, however, that volunteers exhibit consistent, individually characteristic behaviour over extended periods of time. This finding is important, because it demonstrates the inherent subjectivity of sEMG as a form of measurement. This may need to be mitigated in order to explore underlying, subtle relationships between sEMG readings and material or sensory properties of the stimulus. This is an area of work that will be examined in Section 2.4 and exploited in the remainder of this chapter. Moreover, the higher frequency components of the signal are neglected in conventional analysis of sEMG data, which processes raw signals to obtain parameters such as chew intervals, chew rate, and so on. Our findings show, however,, the high frequency region clearly contains non-trivial information, since volunteers can be distinguished by

We return now to discuss the power spectra collected from the consumption of edible gels. Figure 7 showed clearly the between-session and between-volunteer variances. However, coding the symbols by the texture attributes of the gels revealed no conclusive clustering according to the textural properties (not shown); equally unfruitful was an exhaustive search of subsequent PC dimensions. However, this could simply be because any association with the textural properties is small and not clearly seen against the background