Suitably positioned multiple electrodes increase the chances of collecting feedback signals from many regions engaged in the target activity. Attention is a cognitive function with relatively well-known networks of involved brain structures and a popular goal for improvement in many EEG-NFB paradigms. The frontoparietal network (Buschman and Miller, 2007) is one of the brain systems that support attentional mechanisms and it is not surprising that most protocols that successfully increased attention used feedback signals from several EEG-NFB electrodes located at frontal and parietal locations (Allen et al., 2001; Becerra et al., 2012; Enriquez-Geppert et al., 2013), or from a single electrode located over frontal areas (Vernon et al., 2004). In contrast, an experiment with a similar goal, which used C3/C4 electrodes (Boxtel van et al., 2012) to record feedback signals did not achieve significant behavioral changes after EEG-NFB training. We posit that the locations of the EEG-NFB electrodes in many experiments reviewed in this analysis were non-optimally placed for an expected result. It is, therefore, important that future EEG-NFB experiments should identify optimal training electrode locations based on existing knowledge regarding the anatomical and functional substrates of various behavioral tasks. It appears feasible that for a dispersed target network new training paradigms should be developed in which the feedback signals would be weighed by a component analysis from many recording electrodes.

# CONCLUSIONS

We posit that neurofeedback methodology used in the majority of the reviewed experiments did not enable proper targeting of the brain regions responsible for control over the desired behavioral changes. This may explain the lack of correlation

## REFERENCES

between the changes induced in the trained EEG signal and the modification of the targeted behavior, as well as lack of correlation between the remaining analyzed factors and training success. We, therefore, recommend the following factors for improved EEG-NFB training efficacy:

# AUTHOR CONTRIBUTIONS

JR Review concept, data analyses, main conclusions, article drafting. KJ Data analyses, verification of analytical methods, proof reading. KP Data analyses, proof reading. EK Conclusions verification, data analyses, proof reading. RC Data analyses, verification of analytical methods, proof reading. AW Verification of the review concept and of the conclusions.

## ACKNOWLEDGMENTS

This work was supported by the Polish National Science Centre grant 2012/07/B/NZ7/04383.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fnhum. 2016.00301

processing: an SMR neurofeedback training study. Clin. Neurophysiol. 126, 82–95. doi: 10.1016/j.clinph.2014.03.031

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2016 Rogala, Jurewicz, Paluch, Kublik, Cetnarski and Wróbel. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Near-Infrared Spectroscopy-Based Frontal Lobe Neurofeedback Integrated in Virtual Reality Modulates Brain and Behavior in Highly Impulsive Adults

Justin Hudak1 \*, Friederike Blume1 , Thomas Dresler1,2, Florian B. Haeussinger2 , Tobias J. Renner3 , Andreas J. Fallgatter1,2,4, Caterina Gawrilow1,5,6 and Ann-Christine Ehlis1,2

1 LEAD Graduate School & Research Network, University of Tübingen, Tübingen, Germany, 2 Department of Psychiatry and Psychotherapy, University Hospital Tübingen, Tübingen, Germany, 3 Department of Child and Adolescence Psychiatry, University Hospital Tübingen, Tübingen, Germany, 4 Center for Integrative Neuroscience, University of Tübingen, Tübingen, Germany, 5 Department of Psychology, University of Tübingen, Tübingen, Germany, 6 Center for Individual Development and Adaptive Education of Children at Risk, Goethe University Frankfurt, Frankfurt, Germany

#### Edited by:

Soledad Ballesteros, Universidad Nacional de Educación a Distancia (UNED), Spain

#### Reviewed by:

Sabrina Brigadoi, University of Padua, Italy Hasan Ayaz, Drexel University, United States

> \*Correspondence: Justin Hudak

<EMAIL_ADDRESS>
Received: 12 May 2017 Accepted: 08 August 2017 Published: 04 September 2017

#### Citation:

Hudak J, Blume F, Dresler T, Haeussinger FB, Renner TJ, Fallgatter AJ, Gawrilow C and Ehlis A-C (2017) Near-Infrared Spectroscopy-Based Frontal Lobe Neurofeedback Integrated in Virtual Reality Modulates Brain and Behavior in Highly Impulsive Adults. Front. Hum. Neurosci. 11:425. doi: 10.3389/fnhum.2017.00425 Based on neurofeedback (NF) training as a neurocognitive treatment in attention-deficit/hyperactivity disorder (ADHD), we designed a randomized, controlled functional near-infrared spectroscopy (fNIRS) NF intervention embedded in an immersive virtual reality classroom in which participants learned to control overhead lighting with their dorsolateral prefrontal brain activation. We tested the efficacy of the intervention on healthy adults displaying high impulsivity as a sub-clinical population sharing common features with ADHD. Twenty participants, 10 in an experimental and 10 in a shoulder muscle-based electromyography control group, underwent eight training sessions across 2 weeks. Training was bookended by a pre- and post-test including go/no-go, n-back, and stop-signal tasks (SST). Results indicated a significant reduction in commission errors on the no-go task with a simultaneous increase in prefrontal oxygenated hemoglobin concentration for the experimental group, but not for the control group. Furthermore, the ability of the subjects to gain control over the feedback parameter correlated strongly with the reduction in commission errors for the experimental, but not for the control group, indicating the potential importance of learning feedback control in moderating behavioral outcomes. In addition, participants of the fNIRS group showed a reduction in reaction time variability on the SST. Results indicate a clear effect of our NF intervention in reducing impulsive behavior possibly via a strengthening of frontal lobe functioning. Virtual reality additions to conventional NF may be one way to improve the ecological validity and symptom-relevance of the training situation, hence positively affecting transfer of acquired skills to real life.

Keywords: NIRS, neurofeedback, virtual reality, impulsivity, ADHD

# INTRODUCTION

fnhum-11-00425 August 31, 2017 Time: 17:8 # 2

Impulsivity refers to the inability to inhibit behavioral responses to urges created by external stimuli as well as internal desires, often brought about by the current environment. It is a ubiquitous behavioral trait found in healthy individuals as well as those with developmental disorders such as attention-deficit/hyperactivity disorder (ADHD), substance-use disorders, binge eating disorders, and others (Whiteside and Lynam, 2001; Bari and Robbins, 2013). Individual impulsive episodes, such as drunk driving, can negatively impact the lives of the impulsive individual, as well as the lives of others. On neuropsychological tasks, impulsive behavior is associated with certain types of errors, typically on conditions requiring inhibitory control. For example, the more impulsive an individual is, the more commission errors [i.e., false alarms (FA)] they make on go/no-go tasks (Aichert et al., 2012; Weidacker et al., 2016). Impulsive subgroups such as binge eaters (Hege et al., 2014) and binge drinkers (Henges and Marczinski, 2012) also make more FA than healthy controls.

From a neuroscientific perspective, impulsivity is strongly linked with dysfunctional frontal lobe activity and frontal lobe excisions (Fallgatter and Herrmann, 2001; Bari and Robbins, 2013). Development of impulse control is the result of maturation of the cognitive control network (CCN; Casey et al., 2008; Steinberg, 2008; in Shulman et al., 2016) which consists of the lateral prefrontal cortex and its connectivity with other frontal, striatal, motoric, and parietal regions (for comprehensive reviews see Cubillo et al., 2012; Rubia et al., 2013). Highly impulsive subgroups require a stronger activation of the CCN than healthy controls to achieve comparable response inhibition (Horn et al., 2003; Ding et al., 2014). Additionally, evidence for negative correlations between trait impulsiveness and activation as well as connectivity in prefrontal brain structures has been provided (Farr et al., 2012). Furthermore, there is evidence that the bilateral dorsolateral prefrontal cortex (dlPFC) may be involved in inhibitory control as transcranial direct current stimulation (tDCS) of the left dlPFC led to improved inhibitory control on a go/no-go task in participants with ADHD (Soltaninejad et al., 2015).

Neurofeedback (NF), a therapeutic technique in which participants are tasked with regulating their own brain activity, is used as a way to effect long-term change in abnormal brain activity (Arns et al., 2013). Thereby, electroencephalography (EEG)-based NF protocols have shown promise in reducing impulsive symptoms in ADHD (Gevensleben et al., 2012, 2014a). However, these protocols have had mixed effects, particularly as they are often based on brain-frequency imbalances that are highly heterogeneous within subjects (Holtmann et al., 2014). A recently emerging NF protocol for ADHD using functional near-infrared spectroscopy (fNIRS) to measure the blood oxygenation level dependent (BOLD) response within the dlPFC has several potential advantages over traditional EEG protocols (Marx et al., 2015).

Compared to EEG, fNIRS has improved spatial resolution and better correspondence of channel to underlying brain region, as well as reduced sensitivity to movement-based artifacts, making it ideal for NF training of circumscribed brain areas in motorically restless individuals (e.g., ADHD patients, children, etc.). Furthermore, evidence from BOLD-based NF paradigms suggest that they yield effects faster than their EEG-based counterparts. In a pilot study with children with ADHD, significant symptom improvements were found after only 12 sessions of fNIRS-based dlPFC training (Marx et al., 2015). Sherwood et al. (2016) found that – in healthy subjects – achieving control of the BOLD response in the dlPFC is possible after just five sessions of real-time functional magnetic resonance imaging (fMRI) NF training. Current EEG protocols, on the other hand, require between 25 and 50 sessions to realize significant effects (for a review and meta-analysis see Begemann et al., 2016). However, despite the promise of BOLD-based protocols as a potential treatment for impulsivity, such protocols still need to translate from laboratory to real-world settings.

Neurofeedback treatment is often criticized for its lack of ecological validity. Simply put, strategies of brain regulation learned in a lab setting may not translate well into the real world. Those with impulsivity struggle in the classroom where academic achievement is negatively correlated to impulsivity severity (Spinella and Miley, 2003). Therefore, any effective strategies developed in NF therapy should ultimately be applied in the classroom (or a similar real-world) setting, a concept known as transfer (e.g., Strehl, 2014). However, NF protocols – at this point – cannot be utilized in a real scholastic setting as they require large and delicate equipment, and students need to concentrate on the current lesson. An increasingly viable option, virtual reality (VR), has been used for assessment of clinical symptoms of ADHD in the classroom (Muhlberger et al., 2016) and with an EEG-based NF protocol designed to reduce inattentive and impulsive behavior in adolescents displaying behavioral problems (Cho et al., 2004). In the latter study, the VR group showed the greatest improvement following NF training on attention-related tasks relative to both a control group and a 2-D classroom group, but no difference in impulsivity. However, this study was controlled with a waiting group, thus not ruling out non-specific effects of NF training, such as continuous performance monitoring, reinforcement of compliance, and the idea that one is being treated by a sophisticated technology and professional (Gevensleben et al., 2012, 2014b). Furthermore, the NF was a separate module, not incorporated into the experience of the class itself.

Based on these findings, we developed a virtual classroombased fNIRS NF protocol (for study design see Blume et al., 2017) in order to directly facilitate transfer of NF training effects to the classroom. Importantly, feedback is delivered in the form of gentle dimming or brightening of the overhead lighting which does not distract the participant from the experience of being in a classroom. In the present study, we implement a 2 week accelerated protocol in highly impulsive young adults, consisting of eight training sessions (one per day) which were bookended by a pre- and a post-test to assess behavioral changes during a go/no-go, n-back, and stop-signal task (SST). Changes in frontal lobe function were also assessed during the go/no-go and n-back tasks using fNIRS. To control for the previously mentioned non-specific effects of the NF training, we used bilateral musculi

supraspinatus-based electromyography (EMG) biofeedback (BF) (see Marx et al., 2015; Mayer et al., 2015). This method has been successfully used in the aforementioned studies as a control for NIRS-based NF. Sham-based NF control groups (e.g., targeting putatively unrelated brain areas) invite ethical concerns, as training random areas may have unforeseeable negative effects on the participant, who is often recruited on the premise that the training will be helpful to their condition (Holtmann et al., 2014). Furthermore, participants sometimes become aware that they are part of some sham conditions (particularly if the sham feedback contains data completely unrelated to the current training situation, e.g., training data of another participant), or even assume they are part of one when they are not, leading to both drop-outs and reduced motivation, a critical aspect for any successful NF training (Birbaumer et al., 1991; Gevensleben et al., 2014a). As we did not explicitly inform participants that EMG BF was a control condition, they were less susceptible to this motivation loss.

We hypothesize that the fNIRS-based NF group will show an improvement in dlPFC activity during the cognitive tasks (go/no-go and n-back) relative to the EMG-based control group following the treatment program. We also expect the NF group to show a reduction in FA (go/no-go task) as well as reduced stop-signal reaction time (SSRT) on the SST from pre- to post-test measurement (as measures of response inhibition). As secondary outcomes, we expect reaction time (RT) and RT variability [standard deviation of the reaction time (SDRT)] to decrease for the NF group on all tasks, as the dlPFC plays a role in a multitude of executive functions. The expected neurocognitive improvements following frontal lobe focused fNIRS-based NF in a virtual training environment would confirm the general feasibility of a combination of NF with virtual training scenarios which could – in the long run – increase the ecological validity of NF interventions.

# MATERIALS AND METHODS

# Participants

We recruited 22 students from the University of Tübingen out of a larger group of potential participants who had completed the Barratt Impulsiveness Scale (BIS; Barratt, 1959) using an online format. Based on their high BIS scores (MBIS = 85.75, SDBIS = 9.36), these students were selected and invited to an in-person screening for ADHD [according to criteria from the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR; Sass et al., 2003)] using two subtests from the Homburger ADHD Scale for Adults (HASE; Rösler et al., 2008), the German versions of the Wender Utah Rating Scale (WURS-K) and the ADHD-Self Assessment Scale (ADHS-SB). Participants meeting the criteria for an indication of ADHD under this context (WURS-K > 30 and ADHS-SB > 18) were excluded from the study and informed about the outpatient ADHD program at the Department of Psychiatry and Psychotherapy at the University Hospital Tübingen (n = 1). The remaining participants (n = 21; nine female, MAge = 23.4, SDAge = 2.8) reported no history of serious or chronic illness, neurological, or psychiatric disorders.

This study was approved by the Ethics Committee of the Medical Faculty of the University and the University Hospital of Tübingen and all procedures were in accordance with the Helsinki Declaration of 1975, as revised in 2013. Participants provided written informed consent and were compensated with 100 Euros for completing the duration of the training including pre- and post-measurements (10 sessions, 1 h each, over 2 weeks). One participant dropped out of the study due to feeling ill from the VR and was payed pro-rata of 10 Euro per hour participated.

# Study Design

The study followed a randomized, controlled experimental design. Participants were randomized (10 participants in each group) to either eight fNIRS-based NF (experimental) or eight EMG-based BF (control) sessions taking course daily over two weeks (Tuesday to Friday in the first week, Monday to Thursday in the second week). We randomized without stratifying for any other variables. Groups did not differ significantly in gender (NF: 4 female, 6 male; BF: 5 female, 5 male; Fisher's exact test, p = 0.50), or in age (MBF = 22.9, SD = 2.88; MNF = 23.9, SD = 2.77; t(18) = 0.80, p = 0.44). The pre-test and post-test were exactly the same and included a go/no-go task, an n-back task, and an SST. The pre-test took place on the Monday of the first week, while the post-test occurred on the Friday of the second week. Order of the pre- and post-test measures was counter-balanced between subjects.

# Virtual Classroom Scenario

The participants were seated and wore the Oculus Rift (Oculus Rift, United States1 ) VR head-mounted display (HMD). The HMD rendered a virtual classroom developed by KatanaSim (KatanaSim, Germany2 ) with animated students and a teacher. The participants' point of view was seated first-person, facing the teacher (**Figure 1**). The participant had a full 360◦ view from the desk seat, with other students seated nearby. The task was to control the brightness of the lighting in the classroom. When an upward-pointing arrow was shown on the chalkboard, the participant was required to "activate" in order to make the light brighter. When the arrow pointed downward, the participant was required to "deactivate" in order to make the light darker. Briefly, activation requires higher output compared to baseline from the respective feedback source, while deactivation requires reduced output compared to baseline (see below for more details on fNIRS and EMG activation/deactivation protocols). Importantly, participants were not told, in either condition, how to regulate the lighting in the classroom, they were instructed simply to try to increase the lighting in the room when the arrow pointed upward and to decrease the lighting when the arrow pointed downward. In this way, only the positive or negative feedback they received from the scenario should have enforced their learning of the feedback parameter. The probability that a trial was activation (arrow up) was 50% in sessions 1–4 and 80% in sessions 5–8. More activation was encouraged in the second half of the scenario, as more upregulation of the prefrontal cortex is

1www.oculusvr.com

2www.katanasim.com

associated with stronger inhibitory control (Rubia et al., 2013; Soltaninejad et al., 2015). Participants were confronted with distractions within the scenario (e.g., students turning around or cell phones ringing) from the second half of each session until the end.

Before each trial, a baseline and threshold of light fluctuation were calculated to determine the point at which the classroom light was balanced between fully bright and fully dark and the range within which it could fluctuate. Following successful activation or deactivation – when the signal was 60, 70, or 80% of the time above or below the baseline, respectively – the participant was rewarded with one, two, or three smiley faces, respectively, on the chalkboard.

Each session was comprised of three blocks, the first and the last being 12 min in length while the second, the transfer block, was 8 min. In the transfer block, the light's brightness was fixed, meaning that the only feedback came at the end of each trial. Trial number and length varied depending on the feedback source and will be discussed in the following sections.

#### fNIRS

Functional near-infrared spectroscopy records change in oxygenated (O2Hb) and deoxygenated (HHb) hemoglobin relative to a baseline; the amount of local O2Hb infers the amount of local brain activation, via the process of hemodyamic coupling, wherein increases of cortical activation lead to increases in O2Hb and decreases in HHb (Haeussinger et al., 2014). The ETG-4000 continuous Optical Topography System (Hitachi Medical Co., Japan) was used for pre- and post-tests as well as NF sessions.

Our optode montage featured two 3 × 3 optode arrays centered with the innermost channel of the front row of each array placed on F3 (left hemisphere) and F4 (right hemisphere) of the international 10–20 EEG system (Jasper, 1958). Source–detector distances were kept at 3 cm. The optode arrays were rotated 45◦ laterally along the transversal plane so that the innermost four channels in the two frontal rows were oriented over the left and right dlPFC (**Figure 2**). The third optode array was a 3 × 5 arrangement where the most superior and lateral optode on the left and right of the array were oriented on P3 and P4, respectively. Subtending the parieto-occipital cortex, this probeset was used exclusively for common average (CA) reference, a signal correction method (see below).

#### fNIRS Feedback Signal and Trials

The feedback target was the average amplitude of O2Hb within the bilateral dlPFC (see Marx et al., 2015). The raw fNIRS signal was sampled at 10 Hz and preprocessed in MATLAB version 9.0 (The MathWorks Inc., United States). A moving average Kalman filter with a 5 s sliding window was then applied to the data. Finally, we used a CA artifact removal method used in previous NF designs serving as a

basis for this design (Marx et al., 2015; Mayer et al., 2015). This method was preferred because of its ability to remove probeset-wide effects from individual channels (Heinzel et al., 2013). For the CA, the raw average of all 46 channels was subtracted from the raw average of the eight emitter–detector channel pairings over the dlPFC in order to limit the influence of artifacts – e.g., superficial blood flow, head and jaw movements, and respiration – on the hemodynamic response in the feedback channels. All preprocessing occurred online.

The fNIRS trials were 30 s in duration with a 5 s baseline period. Relative O2Hb concentration higher than baseline led to brightening of the lights; concentration lower than baseline led to dimming. Trials were divided into three blocks (**Figure 3**). The first and last blocks contained 12 trials and subsequent rests of 20 s duration. The middle block contained eight trials and rests and was used as the transfer block, wherein no continuous feedback was provided, though participants were still given feedback at the end of the trial. There was no jittering of intertrial intervals.

# EMG Feedback Signal and Trials

Monopolar EMG, with a sampling rate of 1000 Hz, provided feedback from the bilateral musculus supraspinatus for the control group (see Mayer et al., 2015). The signal was referenced to the right mastoid and was grounded on the left mastoid. The data stream was bandpass filtered between 80 and 300 Hz. The resulting signal was then normalized via a maximum output and a resting output, for which the participant flexed both muscles maximally for 10 s and sat completely at rest for 10 s, respectively. At each time point, feedback was equivalent to:

$$\text{Feedback Index = R--1, }$$

where R and L were the right and left normalized muscle outputs, respectively, given by:

R(L) =

Right(Left)EMG Signal−Average Resting Baseline Right(Left) Average Maximal Muscle Output Right(Left) .

Therefore, more tensing of right muscle led to brightening; more tensing of left muscle led to dimming. Baseline for each trial was an average of the last 2 s of the resting feedback signal.

The EMG trials were 15 s in duration with a 2 s baseline period. Relative muscular feedback index higher than baseline led to brightening of the lights; feedback index lower than baseline led to dimming. Trials were divided into three blocks (**Figure 3**). The first and last blocks contained 24 trials and subsequent rests of 10 s duration. The middle block contained 16 trials and rests and was used as the transfer block, wherein no contingent feedback was provided.

#### Pre- and Post-measures Go/no-go and n-Back Task

The go/no-go and the n-back tasks were programmed in Presentation version 18.0 (Neuro Behavioral Systems, United States) following previously published protocols (Mayer et al., 2015; see also Ehlis et al., 2008). We recorded fNIRS during both tasks. Briefly, the go/no-go task consisted of alternating go and no-go blocks (four repetitions each) separated by rest blocks, each block lasting 30 s. In the "go" condition, participants were asked to respond as fast as possible to each stimulus. In the "no-go" condition, participants were instructed to withhold their response on no-go trials (here: presentation of the letter "N"; 25% of trials). Dependent variables were RT, SDRT, FA, and omission errors.

The n-back task consisted of three blocks each of 2-back (high working memory load), 1-back (low working memory load), and 0-back (control) (block length: 30 s; separated by 30 s rest periods). In the 2- (1-)back task, the participants were instructed to press the space bar as quickly as possible whenever the current letter was the same as the letter two letters (one letter) back. In the 0-back task, the participant was instructed to respond when the letter "O" appeared on the screen. Dependent variables were RT, SDRT, and correct hits.

#### Stop-Signal Task

The SST followed the protocol described in Verbruggen et al. (2008). The task consisted of one practice block and three 3-min verum blocks wherein the participant should respond to the direction of an arrow pointing on the screen as quickly as possible. In roughly 25% of trials, the arrow would turn blue,

indicating the participant should withhold their response, after a variable stop-signal delay (SSD) that started at 250 ms and increased or decreased by 50 ms depending on if they failed or succeeded to stop, respectively. Dependent variables in the SST included the SSRT – a measure of behavioral inhibition – RT, and SDRT. The SST was added as a secondary measure for behavior. We did not record simultaneous fNIRS with this measure.

#### Analysis

#### Functional Near-Infrared Spectroscopy Data

All analysis was performed using MATLAB. In order to analyze fNIRS data, we used subroutines programmed in our research group, adapted for fNIRS from the Statistics Parametrical Mapping toolbox for MATLAB (SPM8; Friston et al., 1994). Raw signals were bandpass filtered between 0.01 and 0.2 Hz to remove unwanted physiological artifacts such as heartbeat and respiration. Next, channels exceeding three times the within-subject standard deviation over the course of the measurement were interpolated (see Hagen et al., 2014) using a Gaussian distribution with the O2Hb values of proximal channels given a higher weighting than distal ones; less than 10% of all channels were interpolated. We then applied a wavelet-based transform (Molavi and Dumont, 2012) to detect and correct motion artifacts that were still part of the data. We used the hmrMotionCorrectWavelet algorithm from the Homer2 fNIRS analysis package for MATLAB with the standard motion artifact detection threshold of 1.5 SD above the interquartile range of the data (Huppert et al., 2009). Finally, a block-related average amplitude was calculated for each channel using an interval of 0–60 s after block onset with a 10-s baseline correction. Linear detrending was applied to remove slow drifts in the data. Finally, average amplitudes over the duration of the task blocks (0–30 s) were calculated.

# Region of Interest (ROI)

We mapped fNIRS channels to corresponding, underlying cortical areas based on a virtual registration method (Rorden and Brett, 2000; Singh et al., 2005; Tsuzuki et al., 2007). The left and right dlPFC regions of interest (ROIs) consisted of the channels that we used for the NF training. These channels are concentrated in Brodmann Areas 9, 45, and 46. This includes the dlPFC and also slightly expands into the inferior frontal gyrus (IGF; see **Figure 2**).

# Rate of Learning and Correlation with Primary Outcome Variables

Additionally, we analyzed the success of the participants in obtaining control of the feedback parameter. Our success rate was calculated as the average percentage of time spent in the correct direction of the desired feedback (above or below the baseline, for activation vs. deactivation trials, respectively) for the duration of the trial. An average was calculated for all trials from the first week (four sessions) and the second week (four sessions). The rate of learning was calculated as the average of the second week minus the average of the first week. Rate of learning was then correlated with the primary outcome variables of FA rate in the no-go task and average amplitude of O2Hb of the feedback channels during the no-go task. Similar metrics were created in order to compute the correlations: pre–post FA errors were computed for each subject, to give a metric of individual improvement. Similarly, a post–pre average amplitude of O2Hb of the feedback channels was computed to reflect difference in activation after the training. In the event of significant correlations in one or more groups, we computed a pseudopermutation test (n = 10,000 permutations), permuting the group assignment while keeping within-subject correlation pairs intact, to determine a significant difference between groups. The number of permutations in which the permuted group difference in ρ value was larger than the verum group difference in ρ value was divided by the total number of permutations to create a p-value.

# Statistical Analysis

To evaluate the statistical significance of pre–post changes in O2Hb and HHb in the go/no-go and n-back tasks, we conducted 2 × 2 × 2 × 2(3) repeated measures analyses of variance (ANOVAs), with the between-subjects factor treatment group (NIRS vs. EMG) and the within-subject factors of time (pre vs. post), ROI (left dlPFC vs. right dlPFC), and condition (n-back (3): 2-, 1-, and 0-back; go/no-go (2): go and no-go). For behavioral data, repeated measures ANOVAs were performed using the same factors excluding ROI. When data violated the assumption of sphericity, Greenhouse-Geisser corrected values were reported. For significant main and interaction effects, two-tailed Student's t-tests were employed for post hoc analyses (paired or independent samples, as appropriate). In cases where the assumption of normality was violated, we used two-tailed Mann–Whitney U tests or Wilcoxon signed-rank tests, respectively.

## ROI Specificity

In order to determine specificity of ROIs we used pseudopermutations tests, wherein the mean difference in the average amplitudes from pre to post measurement for a given verum ROI (vROI) for all participants was compared to a pseudo-ROI (pROI) composed of an equal number of randomly chosen NIRS channels. N = 10,000 permutations of pROI were calculated and the resulting p-value was the sum of trials in which the resulting statistic from the vROI was greater than the permuted statistic from the pROI.

# RESULTS

#### Behavioral Data

Only significant results related to the hypotheses are reported here. For a full summary of behavioral data, see **Table 1**.

#### Go/no-go

False alarm errors in the go/no-go task showed a trend with a large effect size for a measurement time∗ group interaction effect

SD, standard deviation; RT, reaction time; SDRT, standard deviation of the reaction time; FA, false alarms; SSRT, stop-signal reaction time.

(F(1,18) = 4.08, p = 0.059, η 2 = 0.185). Post hoc Wilcoxon signed-rank tests revealed a reduction of FA errors from pre to post measurement in the experimental group (Mpre = 4.8, SDpre = 2.4; Mpost = 2.6, SDpost = 1.3; Z = −2.57, p = 0.01), but not in the control group (Mpre = 4.8, SDpre = 2.7; Mpost = 6.0, SDpost = 5.2; Z = −0.30, p = 0.77) (**Figure 4A**). No other interaction effects were observed.

#### Rate of Learning

A one-sample Kolmogorov–Smirnov test rejected the null hypothesis that the learning rates for the first half and second half of the experimental and control groups, respectively, followed a normal distribution (D = 0.65, 0.65, 0.64, 0.65, N = 10 each, and p < 0.05 each). Therefore, Wilcoxon signed-rank tests and Spearman correlations were calculated. For the experimental group, there was no significant difference between first half and second half performance, but a medium effect size indicating better second half performance (Z = 1.48, p = 0.13, r = 0.33). There was, however, a significant difference between first and second half performance for the control group (Z = 2.68, p = 0.013, r = 0.60), indicating a significantly better performance in the second week with a large effect size.

The rate of learning of both groups failed to correlate significantly with post–pre changes in average O2Hb concentration in feedback channels (|ρ| < 0.224, p > 0.05). The rate of learning in the experimental group, however, correlated strongly with size of pre–post reduction in FA (ρ = 0.75, p = 0.013; see **Figure 4B**). Rate of learning in the control group did not correlate with pre–post reduction in FA (ρ = −0.24, p = 0.508). The resulting pseudo-permutation test concluded that there was a significant group difference (p = 0.015).

#### N-Back Task

No significant behavioral interaction effects were observed. Hit rates for each condition were nearly 100% in the pre-test. Furthermore, no FA errors were made in this task. A ceiling effect was evident for this task.

#### Stop-Signal Task

Reaction time variability yielded a significant interaction effect of measurement time∗ group (F(1,18) = 5.39, p = 0.03, η 2 = 0.231), with the experimental group showing significantly reduced RT variability following the training (Mpre = 160.78 ms, SDpre = 64.88; Mpost = 124.13, SDpost = 60.60; t(9) = 2.48, p = 0.035). The control group showed no difference between measurements (Mpre = 125.55 ms, SDpre = 46.13; Mpost = 145.70, SDpost = 61.04; t(9) = 1.04, p = 0.328).

# fNIRS Data

#### Go/no-go O2Hb

We observed a main effect of task (F(1,18) = 11.92, p = 0.003, η 2 = 0.398, mean amplitudes: Mgo = 0.005, SD = 0.033 mm∗mol/l, Mno-go = −0.005, SD = 0.029 mm∗mol/l) and an interaction effect of time∗ task∗ROI∗ group (F(1,18) = 5.63, p = 0.029, η 2 = 0.238). This interaction was caused by a pre to post increase in O2Hb amplitudes of the left dlPFC in the experimental group during the no-go task (Mpre = −0.029, SD = 0.035 mm∗mol/l; Mpost = 0.010,

SD = 0.040 mm∗mol/l; t(9) = −3.63, p = 0.005; see **Figure 5**). In the control group of the same condition, time, and ROI, there was no significant change (Mpre = 0.006, SD = 0.017 mm∗mol/l; Mpost = −0.006, SD = 0.031 mm∗mol/l; t(9) = 1.15, p = 0.281). All other post hoc comparisons failed to reach significance (|t(9)| < 1.837, p > 0.1). The permutation test indicated that this ROI was indeed the focal point for the increase in brain activation. The resulting p-value was equal to p = 0.003, indicating that there is high spatial specificity to the activation, located in the left dlPFC.

#### Go/no-go HHb

We observed no main effects, only an interaction effect of task∗hemisphere (F(1,18) = 5.79, p = 0.027, η 2 = 0.243). Post hoc testing indicated that there was a trend toward a significant difference in HHb activation between the left and right hemisphere in the "go" condition (Mleft = −0.005, SD = 0.023; Mright = −0.013, SD = 0.026; t(9) = 2.07, p = 0.052).

#### N-Back O2Hb

We observed no main effects or significant interaction effects (all |F(2,36)| < 2.50; all p > 0.11).

#### N-Back HHb

We observed a trend for a main effect of task (F(1.39,24.93) = 3.75, p = 0.052, η 2 = 0.173; mean amplitudes: M2Back = −0.011, SD = 0.023; M1Back = −0.007, SD = 0.021; M0Back = 0.001, SD = 0.024). Again, the indication is a higher activation in tasks with a higher working load. We also observed a trend for a main effect of time (F(1,18) = 3.26, p = 0.088, η 2 = 0.153; MPre = −0.008, SD = 0.026; MPost = −0.003, SD = 0.021), indicating a marginal decrease in activation across all tasks from pre to post measurement time. No other main effects or interaction effects were observed.

#### DISCUSSION

The present study was designed to test the efficacy of a novel neurofeedback intervention (fNIRS-based frontal lobe NF in a virtual classroom environment) with the ultimate aim of reducing ADHD symptoms in schoolchildren by increasing their ability to regulate prefrontal cortex activity (Blume et al., 2017). Here, we focused on the effects of this newly developed NF protocol in a sample of highly impulsive young adults, a subclinical risk population that exhibits many of the behavioral abnormalities also seen in patients with ADHD (e.g., Herrmann et al., 2009). In this proof-of-concept study, we were primarily interested in first, whether the fNIRS-based NF group would show increased cortical activation in feedback channels during frontal lobe/impulsivity-related tasks (go/no-go and n-back), following focused training of these channels and second, whether the fNIRS-based NF group would show a reduction in impulsive behaviors (go/no-go, n-back, SST).

During a go/no-go task, we observed a significant increase compared to a pre-training baseline in cortical O2Hb concentration in the left dlPFC of the experimental (fNIRS) group only. During the same task, we observed a concurrent and significant reduction in FA errors of the same group. Importantly, this reduction in FA errors correlated significantly with the rate of learning of the experimental subjects but not the control subjects. Additionally, we observed a reduction in RT variability on the SST for the experimental group. We observed no group differences in either cortical activation or behavior on the n-back task. The lack of a group difference after training on this task is likely due to the study specifically focusing on the recruitment of highly impulsive students. There is no evidence to suggest that highly impulsive participants have explicit deficits in working memory. In fact, in a study examining the correlations between trait impulsivity (as measured by BIS self-report) and performance on various neurocognitive tasks, no significant correlation was found between trait impulsivity and working memory performance, while trait impulsivity correlated strongest with go/no-go errors (Keilp et al., 2005). Furthermore, task accuracy reflected a ceiling effect from the pre-test, indicating that the task was not difficult for these

in black. (B) ROI event-related averages. Circled regions from (A) indicate the left dlPFC ROI for which the event-related average of O2Hb ± standard error of the

subjects. Therefore, despite the potential benefit to working memory that training the dlPFC might imbue, in our case there may have been no deficit to correct. Lastly, HHb data showed no differences in activation in either task. These results make sense in the context of the NF training; since O2Hb was trained, the hypothesis would be that O2Hb and not HHb would show the strongest pre–post effects. In addition, O2Hb is more sensitive to detection of changes than HHb (Strangman et al., 2002).

mean (SEM) is depicted for both pre-(blue) and post-(red) tests.

False alarm errors, or incorrect go-responses to no-go stimuli, represent a failure to exhibit response inhibition (Aichert et al., 2012), an impulsive trait that subjects with ADHD share with highly impulsive participants. A reduction for the experimental group and not for the control group suggests that the fNIRS intervention was effective in reducing impulsive behavior as specified. The strong O2Hb correlation observed between a reduction in FA errors and the rate of learning within the experimental group, but not within the control group, further illustrates the importance of specificity in NF training. The goal of actually learning to control the feedback parameter is often overlooked in NF studies, where the rate of obtained control is rarely reported (Zuberer et al., 2015). Interestingly, the control group showed a significant improvement between the first and second week in regulating the feedback parameter while the experimental group did not. This likely has to do with the comparable ease of the EMG feedback; once one learns the correct movement, it can relatively easily be replicated every trial. The fNIRS feedback is likely more complex, as there is no right or wrong way to achieve the feedback parameter, and sustaining oxygenation of the dlPFC over time is strenuous. Given this complexity, the medium effect size observed in the fNIRS learning rate is encouraging, and may simply mean that more sessions are needed to fully gain control. Moreover, for

the specific sample investigated and trained here (i.e., highly impulsive subjects), frontal lobe alterations have been shown as a central neurophysiological correlate, so it is perhaps not surprising that improving control over this area of the brain seems to have been particularly difficult. However, this behavioral effort seems to pay dividends, as we see that the more control impulsive subjects were able to gain over the activation of their dlPFC, the fewer FA errors they made, whereas the successful learning of the EMG parameter had little effect. This result supports the findings of an fNIRS study that sought to differentiate the roles of the medial and lateral prefrontal cortex during a go/no-go task. The bilateral middle frontal gyrus (i.e., the dlPFC) was responsible for error monitoring during the motor inhibition segment of the go/no-go task (Rodrigo et al., 2014). Our results indicate that the combination of both correct feedback parameter (i.e., frontal lobe focused) and successful learning of that parameter, not one or the other in isolation, is important to the feedback's overall success.

The task-specific increase in prefrontal oxygenation coinciding with a reduction in FA errors suggests that – following the frontal fNIRS training – the highly impulsive participants were able to recruit more cognitive resources, particularly from the dlPFC, during this task, leading to improved performance. Whether or not this was intentional is a matter of debate, but the goal of NF interventions remains to train implicit activation of brain activity through operant and classical conditioning (Strehl, 2014). Therefore, it seems that the participants were able to transfer skills learned either implicitly or explicitly from the training into a performance situation. Furthermore, this increase in cortical activation was both task- (no-go) and region-specific (left dlPFC). While there was no increase in activation in the right dlPFC, the left-specific increase as well as the increase in inhibitory control are in line with the tDCS study of Soltaninejad et al. (2015) who used cathodal stimulation over the left dlPFC of adolescents with ADHD and observed a decrease in FA errors. While the literary consensus places the locus of inhibitory control within the right dlPFC, inferior prefrontal, premotoric, and striatal brain structures (Aron et al., 2004, 2014; Bari and Robbins, 2013; Obeso et al., 2013), the left dlPFC shares strong functional connectivity with the above-mentioned areas (Ridderinkhof et al., 2004; Aron et al., 2014). Moreover, the dlPFC does not seem to be directly responsible for inhibitory control, but rather functions as a higher order mechanism that organizes the relevant brain structures above when attention control or increased working memory capacity is needed, in particular for oddball or complex no-go tasks (Criaud and Boulinguez, 2013). Because our go/no-go paradigm could be considered oddball, with an occurrence of no-go stimuli in only 25% of trials, it may be that the extra dlPFC resources recruited were used for focusing attention, rather than inhibitory control per se. Indeed, the reduction in SDRT seen in the SST also indicates an increase in attentional resources, possibly also mediated by an increase in prefrontal brain activity, though NIRS data were not available for this task. Increases in SDRT are generally considered to be related to lapses in attention (Alderson et al., 2007), though Kirkeby and Robinson (2005) found SDRT to be inversely correlated with trait impulsivity. Still, this does not rule out the idea that our impulsive sample also suffered from inattentiveness.

Treatment effects for both impulsivity and possibly inattention are encouraging from a translational perspective regarding potential use of our NF design with an ADHD population. We chose the dlPFC as a NF site because of its involvement in general top-down cognitive control, and the realization of significant training effects in impulsivity and possible inattention suggests that the protocol may be useful for an ADHD population. Several reasons lead us to be hopeful of even greater effects in a current study in our lab with ADHD schoolchildren (Blume et al., 2017). First, the sample size of this study was small. Only large effects could be detected, and with a greater sample size, we would expect to see effects in a wide range of other cognitive and behavioral deficits. Secondly, the training was compact and about half the number of training sessions we would recommend (and currently use) for a clinical ADHD project. As far as we know, this is the shortest number of training sessions to produce effects in brain activation and behavior that was adequately controlled for specificity. Cho et al. (2004) also used a 2 week, eight session NF paradigm with EEG and found training effects for inattention and impulsivity, but they did not have an adequate control group (waiting group), and additionally, did not measure differences in brain activity pre and post. Lastly, but most importantly, children have a greater capacity for brain plasticity than adults (Kolb and Gibb, 2011). For children with ADHD, this capacity is even more pronounced within the dlPFC, a region that develops particularly late for them (Rubia et al., 2013). Given the current study's results, we would expect even greater improvements within a child population.

The current study was limited by several factors, which we hope to improve upon in a second study with children with an ADHD diagnosis (Blume et al., 2017). The sample size was small which limited data analysis. Our aim was to test the viability of an immersive VR NF paradigm, and it appears that the full classroom immersion did not detract from the ability of the participants to regulate their brain activity. There was a difference between experimental groups in pre-test no-go activation, with the experimental group showing less activation than the control group. Small groups, even with proper randomization, have a much greater chance of having differing baseline measurements simply due to sampling error (Marshall, 1996). The larger the group, the smaller the chance of pre-baseline differences due to a random sampling error. As NF studies require large time and monetary investments per participant, and the aim of our study was to ultimately test the efficacy of VR NF, we chose 10 participants per group as a balance between power and realism. For technical reasons, we did not have triggers to compare the extent to which participants were able to regulate their brain activity across sessions, something that will be improved in the next study. While we used distractors in the current study, there was no way to compare trials in which a distractor occurred to trials in which they did not. Furthermore, we lack a comparison of the effects of the immersive VR NF paradigm to a 2-D version. In an ongoing study with children with ADHD (Blume et al., 2017),

we include a 2-D group that still uses lighting in the classroom as the feedback source, but the child sees the classroom on a normal computer monitor. In this way, we will be able to determine if immersive NF is actually more effective for the transfer of the learned regulation. Furthermore, the classroom itself is only one of many possible VR NF designs. Virtual reality scenarios coupled with NF are limited only to the imagination and relevance to a certain psychological disorder. Virtual reality NF with subjects with social phobias, for example, could be integrated within a potentially stressful social situation, like a bar or dinner party, furthering the ecological validity of the treatment while also avoiding an exposition-driven therapeutic approach that cannot be as easily controlled.

Considering these limitations and the relative ease with which they could be improved upon going forward, it seems that VR NF is a very promising modality for the treatment of behavioral disorders with known pathophysiological alterations.

#### ETHICS STATEMENT

This study was approved by the Ethics Committee of the Medical Faculty of the University and the University Hospital of Tübingen and all procedures were in accordance with the Helsinki Declaration of 1975, as revised in 2013.

# AUTHOR CONTRIBUTIONS

All authors have approved of the final version of this manuscript. JH study design, data collection and analysis, and manuscript preparation; FB study design, data collection,

#### REFERENCES

and manuscript preparation; TD study design and manuscript preparation; FH data analysis; TR study design and manuscript preparation; AF study design and manuscript preparation; CG study design and manuscript preparation; and A-CE study design, data analysis, and manuscript preparation.

# FUNDING

This research was funded by the LEAD Graduate School & Research Network (GSC1028), a project of the Excellence Initiative of the German federal and state governments. JH and FB are doctoral candidates and TD is a Junior Research Group Leader at the LEAD Graduate School & Research Network. A-CE was partly supported by the Interdisciplinary Center for Clinical Research (IZKF) Tübingen (Junior Research Group, grant 2115-0-0). We acknowledge support by the Deutsche Forschungsgemeinschaft and the Open Access Publishing Fund of the University of Tübingen.

# ACKNOWLEDGMENTS

The authors would like to give additional thanks to those who contributed with measurements, in particular Marc Werth, Silke M. Bieck, and Nana Ambs. Sandru Razvan was influential in editing and structuring of the manuscript. Additionally, uncompromising support was given by Betti Schopp and Ramona Taeglich in coordinating measurements and training. They would also like to thank the participants for their participation and a very involved study.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Hudak, Blume, Dresler, Haeussinger, Renner, Fallgatter, Gawrilow and Ehlis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Computer Enabled Neuroplasticity Treatment: A Clinical Trial of a Novel Design for Neurofeedback Therapy in Adult ADHD

Benjamin Cowley 1, 2 \*, Édua Holmström3 , Kristiina Juurmaa2 , Levas Kovarskis 3 and Christina M. Krause2

1 BrainWork Research Centre, Finnish Institute of Occupational Health, Helsinki, Finland, 2 Cognitive Brain Research Unit, Cognitive Science, Institute of Behavioural Sciences, University of Helsinki, Helsinki, Finland, 3 Formally affiliated with Faculty of Behavioural Sciences, University of Helsinki, Helsinki, Finland

Background: We report a randomized controlled clinical trial of neurofeedback therapy intervention for ADHD/ADD in adults. We focus on internal mechanics of neurofeedback learning, to elucidate the primary role of cortical self-regulation in neurofeedback. We report initial results; more extensive analysis will follow.

Methods: Trial has two phases: intervention and follow-up. The intervention consisted of neurofeedback treatment, including intake and outtake measurements, using a waiting-list control group. Treatment involved ∼40 h-long sessions 2–5 times per week. Training involved either theta/beta or sensorimotor-rhythm regimes, adapted by adding a novel "inverse-training" condition to promote self-regulation. Follow-up (ongoing) will consist of self-report and executive function tests.

#### Edited by:

Soledad Ballesteros, Universidad Nacional de Educación a Distancia, Spain

#### Reviewed by:

Lutz Jäncke, University of Zurich, Switzerland Juliana Yordanova, Bulgarian Academy of Sciences, Bulgaria

> \*Correspondence: Benjamin Cowley

<EMAIL_ADDRESS>
Received: 13 January 2016 Accepted: 22 April 2016 Published: 09 May 2016

#### Citation:

Cowley B, Holmström É, Juurmaa K, Kovarskis L and Krause CM (2016) Computer Enabled Neuroplasticity Treatment: A Clinical Trial of a Novel Design for Neurofeedback Therapy in Adult ADHD. Front. Hum. Neurosci. 10:205. doi: 10.3389/fnhum.2016.00205 Setting: Intake and outtake measurements were conducted at University of Helsinki. Treatment was administered at partner clinic Mental Capital Care, Helsinki.

Randomization: We randomly allocated half the sample then adaptively allocated the remainder to minimize baseline differences in prognostic variables.

Blinding: Waiting-list control design meant trial was not blinded.

Participants: Fifty-four adult Finnish participants (mean age 36 years; 29 females) were recruited after screening by psychiatric review. Forty-four had ADHD diagnoses, 10 had ADD.

Measurements: Symptoms were assessed by computerized attention test (T.O.V.A.) and self-report scales, at intake and outtake. Performance during neurofeedback trials was recorded.

Results: Participants were recruited and completed intake measurements during summer 2012, before assignment to treatment and control, September 2012. Outtake measurements ran April-August 2013. After dropouts, 23 treatment and 21 waiting-list participants remained for analysis.

Initial analysis showed that, compared to waiting-list control, neurofeedback promoted improvement of self-reported ADHD symptoms, but did not show transfer of learning to T.O.V.A. Comprehensive analysis will be reported elsewhere. Trial Registration: "Computer Enabled Neuroplasticity Treatment (CENT)," ISRCTN13915109.

Keywords: neurofeedback, attention deficit/hyperactivity disorder, attention deficit disorder, adult, randomized controlled trial, waiting list control, learning curves, learning transfer

#### 1. INTRODUCTION

Attention Deficit/Hyperactivity Disorder (ADHD) is a neurobiological condition which can strongly affect several areas of life: lower socio-economic status, less satisfaction with employment and marriage, as well as common co-occurrence of conditions like addiction and depression. Epidemiology research has estimated the prevalence of ADHD among adults to be 4.4%. Subtypes of ADHD have been identified including Inattentive (ADHD-I), Hyperactive (ADHD-H) and combined (ADHD-C). However, the nature of the disease and most effective method of treatment are still not well understood.

This open-label clinical trial is a test of neurofeedback (NFB) as a treatment intervention for ADHD or ADD-diagnosed adults1 . The intervention is based on the NFB training regimes "theta-beta" (TB) and "sensorimotor rhythm" (SMR). These regimes train self-regulation of power in specific bands of the EEG frequency spectrum, through the principle of operant conditioning. The trial also includes the novel addition of a third type of training, "inverse training," designed to help investigate the relationship between NFB learning performance (the requirement to self-regulate) and the specific effects of each training regime. This follows recent calls for increased focus on the internal mechanics of the NFB process by Gevensleben et al. (2014) and Zuberer et al. (2015), in order to attempt more than just another simple test of efficacy.

We test intervention efficacy in a between-subjects manner using a waiting-list control (WLC) group. The trial thus relies on three different levels of measurements: neurophysiological, cognitive, and behavioral; and examines their relationship across multiple time-points. Transfer of learning is measured by: questionnaires tapping ADHD/ADD symptoms that participants filled out at four time-points during the intervention; and by performance on a continuous performance test. More importantly, given our focus on mechanics, we also test specific effects of the NFB training regimes by analysing within-subjects learning curves (LCs) of the treatment group, which represent how they learn to self-regulate their EEG-band activity.

At the time of writing this is an ongoing two phase trial, where phase 1 included intake measurements, NFB intervention, and outtake (all complete), and phase 2 will include follow-up measurements and WLC treatment opportunity (pending). In addition to the protocol structure, we report an initial group-comparison result from the first phase, showing mixed outcomes for efficacy. This result is primarily included to illustrate the importance of the planned withinsubjects analysis for obtaining clear insights from the data gathered.

#### 1.1. Attention Deficit/Hyperactive Disorder

Models seeking to explain the cognitive neuropsychological problems associated with ADHD include disturbance of attention, cortical arousal, and executive functions (for review see e.g., Sergeant et al., 2003; Seidman, 2006). However, a meta-analysis by Huang-Pollock and Nigg (2003) discarded the explanatory value of attention, at least in individuals diagnosed with the combined subtype of ADHD. Increasingly, ADHD is not seen as a disorder of attention at all but as a disorder in key aspects of self-regulation and executive functions (Nigg, 2005). One caveat is the growing consensus that the executive function "single deficit" model cannot sufficiently explain ADHD (Nigg, 2005; Pennington, 2005; Sonuga-Barke, 2005). Studies indicate that not all persons with ADHD have executive function deficits — at least as measured by laboratory tests.

Cortical arousal models in ADHD are closely related to the attentional concept of alerting as proposed by Posner and Petersen (1990), reflecting right-lateralized vigilance network with noradrenergic involvement. These models emphasize deficiencies in the early stages of information processing as a result of under-arousal in cortical systems (Sergeant et al., 1999). EEG and ERP findings tend to support this model in that they reveal excess slow-wave activity in adults with ADHD (Bresnahan et al., 1999). Support also comes from consistent findings of deficit in the continuous performance test (CPT) dprime parameter, which can be considered a consensus index of arousal (Losier et al., 1996). Epstein et al. (2003) found that the d-prime demonstrated very robust relationships to the 18 DSMV-IV ADHD symptoms.

EEG studies suggest that ADHD might stem either from a maturational lag or a developmental deviation (Barry et al., 2003). Maturational lag models require that EEG measures from an individual with ADHD would be considered normal in a younger person, and implies that ADHD adults grow out of their immature EEG activity with increasing age (Mann et al., 1992). Whereas in the developmental deviation model, ADHD is conceptualized as resulting from an abnormality in the functioning of the central nervous system, unlikely to change without targeted intervention.

**Abbreviations:** ADHD, Attention Deficit/Hyperactivity Disorder; ADD, Attention Deficit Disorder; BCIA, Biofeedback Certification International Alliance; CENT, Computer Enabled Neuroplasticity Treatment; CNV, Contingent Negative Variation; IBS, Institute of Behavioral Science; MCC, Mental Capital Care (clinic); NFB, neurofeedback; RCT, randomized controlled trial; SCP, Slow Cortical Potentials (neurofeedback training regime); SMR, SensoriMotor Rhythm (neurofeedback training regime); TB, Theta/Beta (neurofeedback training regime); T.O.V.A., Test Of Variables of Attention; UoH, University of Helsinki; WLC, waiting list control.

1The ICD-10 diagnosis classification system is in use in Finland where the study was conducted. Both ADD and ADHD are used as diagnoses, the former referring to the predominantly inattentive type, as opposed to predominantly hyperactive or combined types.

Longitudinal studies, reviewed by Bresnahan et al. (1999), that followed participants up till adulthood revealed that although there is a significant reduction of slow wave activity in both the ADHD and the control group with increasing age, absolute and relative theta activity remained elevated through adolescence into adulthood (Bresnahan and Barry, 2002). Interestingly, with increasing age, the level of beta activity produced by adults with ADHD was normalized in the frontocentral regions. The most consistent finding from EEG studies of ADHD in adults is increased absolute power in theta, clearly visible in frontocentral areas (Bresnahan et al., 1999; Lazzaro et al., 1999; Clarke et al., 2001). Such findings contradict the maturational lag model, as the difference in slow activity does not disappear with increasing age.

By contrast, the hypo-arousal developmental deviation model originally proposed by Satterfield et al. (1974) has been supported by cerebral blood flow and positron emission tomography studies (Lou et al., 1989; Zametkin et al., 1990). This model proposes that ADHD results from cortical under-arousal, and the observed atypical slow wave activity confirms the existence of altered brain activity among adults with ADHD. Hypo-arousal is thought to correlate with both beta and SMR (sensory motor rhythm, also called low beta), because in normal functioning, increased beta is associated with mental activity, and decreased SMR with physical activity.

Much of the existing research has identified maturational lag or hypo-arousal as the underlying cause of ADHD. Although these models have initiated extensive research, they have failed to clarify the aetiology of the disorder (Bresnahan et al., 1999). Thus, the literature suggests that, at least in adult ADHD, aetiological specificity is lacking; with the consequence that traditional treatments targeted at ADHD as a single disorder are unlikely to be reliable. In contrast, personalized medicine emphasizes heterogeneity within a given disorder, relying on biomarkers or endophenotypes to guide different treatments.

## 1.2. Neurofeedback

NFB, also called EEG biofeedback, is operant conditioning of specific temporal, spatial and frequency features extracted from scalp-recorded electrical potentials (Lubar and Shouse, 1976). Feedback is presented to the treated individual in the form of positive and negative reinforcers (in this study: visual reinforcers) whenever their ongoing EEG features meet or fail to meet a predefined criterion. The aim of NFB is to learn to gain control of those EEG features over time.

Literature supports the efficacy of NFB for children with ADHD (Arns et al., 2009, 2014b; Micoulaud-Franchi et al., 2014). Part of its value is that NFB can be personalized to suit the specific clinical presentation, provided that there is requisite theoretical and observational data to guide the personalization.

NFB has been described as a mechanism that can stimulate cortical arousal and/or regulate cortical oscillations, which in turn may influence such cognitive activity as attention (Vernon et al., 2003). The specific effect has been described variously as following one of two models, termed by Gevensleben et al. (2014) as "conditioning- and-repairing model" vs. "skill-acquisition model." This implies that the effect of NFB may be to repair a presumed cause of disorder to normalize behavior, or instead may be a tool to enhance cognitive performance (see Gevensleben et al., 2014 for a thorough discussion).

It has been suggested that, besides the neurophysiological aspects of NFB, treatment outcome depends greatly on the subjective involvement of the patient. Calderon and Thompson (2004) have conceptualized biofeedback as a three-step process that consists of

The first two steps of the model — becoming aware and learning to control the electrical activity of the brain — constitute NFB learning. The third step refers to transfer of the NFB learning, measured here by performance on a neurocognitive test as well as self-reported ADHD related symptoms.

This study employs two kinds of separate NFB training regimes: theta/beta and sensimotor-rhythm. Additionally, we included a novel "inverse mode" of training, which is a modification of each of these two regimes. Finally, transfer trials during which the patient is given no visual feedback were included toward the end of the trial.

Theta/beta (TB) training regime assumes a theta power that is elevated above normal, and therefore uses an inhibition target for theta power and a reinforcement target for beta power. EEG recording is often at a frontal site. The rationale behind TB training has been described in at least two different ways: as the rectification of cortical hypoarousal (Barry et al., 2003), and as the reinforcement of working memory (Vernon et al., 2003).

Sensimotor-rhythm (SMR) training regime reinforces beta power, usually low or mid beta, often with an inhibition target for theta. The site is above the sensorimotor strip, often lateral, such that the beta oscillations correspond to the sensorimotor rhythm. The rationale for SMR training has been proposed as either facilitating attention (Vernon et al., 2003), or the improvement of sleep through an increase in beta spindles, with concomitant effects on cognitive function (Arns et al., 2014a).

It is important to note, that NFB learning is anchored in two scientific theories, but occurrence of NFB learning as such tests only one of these. On the one hand, NFB learning relies on the cortical arousal model of ADHD that emphasizes underarousal in the cortical systems with excess slow wave activity affecting information processing (Sergeant et al., 1999; Barry et al., 2003). Based on this model, the NFB training aims at increasing fast-wave activity (in this study: SMR and beta bands) and decreasing slow-wave activity (in this study: theta) (Barry et al., 2003). However, NFB learning as such does not test whether the underlying problem of ADHD is under-arousal; what it does test is the operant conditioning of EEG activity. NFB learning is conceptualized in terms of changes in the amount of time a patient manages to move his/her EEG features in the required direction during training sessions as a result of learning to selfregulate cortical oscillations. Zuberer et al. (2015) argue that NFB outcomes should be tested by examination of the learning curves.

Thus, it is clear that, although aetiological and thus clinical specificity for ADHD is lacking, all NFB treatment regimes share a common goal of promoting self-regulation. On the other hand, some more modern NFB regimes have a more explicit approach to self-regulation than their older cousins. For example, Slow Cortical Potentials (SCP) training uses two opposed cortical regulation targets (Mayer et al., 2013), to be trained in random consecutive order. The two most common NFB training regimes TB and SMR do not include such an explicit set of counter-poised targets to induce self-regulation, relying instead on a single target of reinforcement/inhibition, which is trained repeatedly.

The target of SCP training is the Contingent Negative Variation (CNV) Event Related Potential, which Mayer et al. defined as a slow negative shift over central sites that develops following the presentation of warning stimulus while expecting an imperative stimulus that requires a response (Mayer et al., 2015). Thus, while SCP directly addresses self-regulation, it does so only for a single correlate of attention. Other cortical correlates of attention processes are addressed by other NFB training regimes, e.g., cortical hypo-arousal in TB, or spontaneous motor activation in SMR. However, these training regimes have no specific component designed to promote self-regulation.

Therefore, to the standard TB and SMR training regimes we have introduced a mode of "inverse training" (denoted iTB and iSMR), in order to explore the effect of adding an SCPlike approach to these unidirectional training regimes. This takes the form of an extra target in each regime, where the reinforcer/inhibitor is the exact opposite of the norm (see Methods).

TB and SMR training regimes are based on sub-second frequency-band features, so they are not directly comparable with SCP which feeds back the time domain DC component. However, from our point of view, there are at least three motivating reasons to test the "inverse training" self-regulatory modes of TB and SMR.

First, the neurological effect of TB and SMR remains unclear, due to the competing explanatory models. This study will not lay all questions to rest, but it does pursue a novel line of inquiry. Second, "inverse training" should aid the subjective experience of self-regulation, as it does in SCP; after all from the clinical point of view, NFB training in any training regime relies on the patient's own "mental strategy," reinforced by feedbackfree transfer trials. Third, combining the above two issues, the participants have the opportunity to learn the experiential correlate of the inverse neural state, and thus learn to be able to activate OR deactivate cortical resources at will. If we accept, for example, that TB trains the activation of cortical arousal, then the experiential correlate of inverse TB (iTB) might be more appropriate when the individual needs to enter a state of calm reflection. Similarly, SMR implies activation of the sensorimotor strip, which in turn implies quietude of bodily motor-neuron activity; however inverse SMR (iSMR) might be more appropriate when particular task activity calls for so-called kinaesthetic intelligence. This conceptualization of the process would follow the "skill-acquisition" model of Gevensleben et al. (2014). That is, the patient would gain a tool to enhance cognitive performance, as opposed (or in addition) to repairing a presumed cause of disorder.

Although earlier work (Lubar and Shouse, 1976; Monastra et al., 2005), including meta-analysis by Snyder and Hall (2006), has shown support for a single-trait model of ADHD (an elevated theta-beta ratio), others have argued that research results and clinical application should be interpreted with more regard for variability of individuals (Arns et al., 2008). Hammond (2010) goes into this issue in detail, illustrating the heterogeneity in quantitative EEG (qEEG) patterns associated with symptoms and discussing the requirements and need for qEEG analysis guided by normative databases. Johnstone et al. (2005) provided a review of such databases, along with a review of qEEG profiles, which are manifestations seen between genome and behavioral that they term "intermediate" EEG endophenotypes. They called for QEEG endophenotype-guided NFB treatments to provide non-pharmacological interventions to help the subgroup of nonresponders to traditional treatments, or complement traditional treatments in certain cases.

Especially in adults, who are subject to maturation effects across a broad age range, ADHD is a heterogeneous disorder with an uncertain treatment situation. In other words, some might have executive function deficits and might possibly benefit from TB over the prefrontal cortex; while some might benefit more from the characteristic behavioral correlate of SMR, that is, immobility as well as reduction of muscular tension (Chase and Harper, 1971; Howe and Sterman, 1972), thus facilitating the self-regulation of attention through mechanisms similar to mindfulness meditation (Zylowska et al., 2008). For this reason, in this study TB and SMR training regimes are assigned in a personalized fashion based on EEG spectral profiles (see Methods).

#### 1.2.1. Outline

In the rest of this paper, following the CONSORT guidelines, we first document the Methods and design of the trial, including participant criteria, intervention details, objectives, outcome measures, sample size calculation, randomization procedure and other allocation details, plus statistical analysis. Next, we provide existing results from the trial as it stands, primarily regarding how the treatment stage was run, along with preliminary analyses of group comparison outcomes. Finally we discuss issues arising from the trial design and implementation, as well as the implications of the preliminary analyses and future work.

# 2. METHODS/DESIGN

# 2.1. Participants

Inclusion criteria were scores on Adult ADHD Self Report Scale (ASRS) (Kessler et al., 2005), and Brown -ADHD scale (BADDS) (Brown, 1996) indicating presence of ADHD, as well as:

Exclusion criteria included extreme outlier scores in the scales of

• Generalized Anxiety Disorder (Spitzer et al., 2006),

Thresholds for exclusion were not fixed but at the discretion of the consulting psychiatrist. Use of medication for ADHD was not an exclusion criterion but participants were asked not to make changes in medication during the time of the training. Informed consent was obtained from each subject in accordance with the Declaration of Helsinki.

Self-report tests were distributed and submitted by mail. Psychiatric consultations were performed at the private practice of the psychiatrist; IQ testing was performed at the testing room of the University of Helsinki (UoH) Institute of Behavioral Sciences (IBS); both in central Helsinki.

#### 2.1.1. Ethical Approval

Written informed consent for participation was obtained from all participants before entering the study. The protocol followed the Declaration of Helsinki for the rights of the participants and the procedures of the study. An ethical approval of the present research protocol for all participants was obtained from The Ethical Committee of the Hospital District of Helsinki and Uusimaa, 28/03/2012, 621/1999, 24 §. Participants were not remunerated.

#### 2.1.2. Clinic of Treatment

The clinic for intervention sessions was required to be centrally located in Helsinki, to be staffed by licensed psychiatrist in case of emergencies, and to have recognition by the Association for Finnish Work. Technicians were required to have a primary degree in a discipline related to human psychology; they were also required to take a 3-month training course provided at the UoH based on principles of the Biofeedback Certification International Alliance (BCIA).

#### 2.2. Intervention

The experimental treatment was a novel neurofeedback (NFB) intervention, based on the well-known operant conditioning NFB training regimes "theta-beta" (TB) and "sensorimotor rhythm" (SMR); with the novel addition of a self-regulatory component designed to address the heterogeneity and aetiological uncertainty of ADHD in the adult population. The comparator was a WLC group. The WLC design places a randomly assigned control group on hiatus, while the active treatment is applied to the randomly assigned treatment group. The WLC group should receive treatment after the follow-up assessment at 24 months post-treatment, without experimental oversight.

Participants who volunteered in response to advertisements were recruited at time T0. Contact with the psychiatrist and psychologist for screening followed at time T1.

Successfully screened participants were taken to the intake measurement at time T2, where they performed the T.O.V.A. test along with eyes-open and closed baselines, while scalp EEG was recorded. The individual alpha peak frequency (IAPF) of each participant was estimated from band power analysis of eye-opened and eye-closed baseline conditions (Lansbergen et al., 2011). The boundaries of each EEG frequency band for each participant are defined with respect to IAPF, e.g., theta is IAPF×0.4 to IAPF×0.6.

After randomization between treatment and WLC groups at time T3, we assigned participants in the NFB treatment group to either TB or SMR training based on their IAPF-adjusted theta/beta ratio. Those with theta/beta ratio >1 (n = 9) received reinforcement for simultaneous increase in beta and decrease in theta (over power estimated from per-session baseline) at electrode Fz. The rest (n = 16) got reinforcement for increase in SMR and decrease in theta at electrode C4. Band powers within the NFB training regimes are adjusted by IAPF.

Treatment was administered using the following hardware and software set up. The EEG amplifier was the Enobio ambulatory device (Neuroelectrics SL, Barcelona)2 , with streaming Bluetooth connection to standard Windows 8 desktop computers. The software was developed within the project, as described in Cowley et al. (forthcoming). Briefly, the system is based on OpenViBE signal acquisition framework3 , with a Qt frontend, and is available open source4 .

NFB interventions were standardized by scheduling of the training sessions: session duration was fixed; and training blocks per session, sessions per week, timing of the break from training, and total duration of training were all constrained to equalize the intervention. At time T4, treatment group participants began their treatment by being briefed about all aspects of the NFB training regimes, e.g., length, frequency, purpose. Finally for the first phase, outcome measures were taken at time T5, when all participants in the treatment group had completed 40 sessions NFB.

In the first phase the care providers were monitored by both the lead researcher and responsible psychiatrist on separate occasions, with interviews to ascertain their selfassessment of performance. Both care providers and patients were given self-assessment questionnaires to describe their working relationships.

The second phase of the trial is ongoing at time of writing. Beginning with re-recruitment at T6, phase two consists of a follow-up measurement for all, and treatment option for WLC participants. Follow-up measurements include the ASRS and BADDS self-reports. Treatment for the WLC group will not be NFB, but will consist of a game-like computerized attention training intervention, without concurrent recording of EEG.

## 2.3. Objectives

Our RCT research questions (RQs) follow Calderon and Thompson (2004), since we first examine NFB learning "within subjects," and second examine the transfer of NFB learning

2http://www.neuroelectrics.com/.

3http://openvibe.inria.fr/.

4https://github.com/CBRUhelsinki/CENTplatform.

comparing the treatment group to a WLC group. Within this paper, we report only first-stage analysis, namely the comparison between groups addressed by H2a and H3c below (see Results).

#### 2.3.1. Learning in NFB

The NFB learning metric reflects the proportion of time during training when EEG signals are in the target state; the "learning curve" is thus characterized by a signal evolving over blocks and sessions of training. The shapes or slopes of participants LCs are rarely reported in the NFB literature: analysis tends to focus on transfer outcomes compared to a control group. However, clinical observations commonly indicate that learning occurs. Also, NFB learning in this study was manipulated with the addition of the "inverse-training" mode. The LCs resulting from normal, inverse and transfer training blocks should each be slope-positive, because they each require a similar act of concentration which the participants are practicing throughout training. Finally, the profile of the LCs over sessions which combine all training types should be slope-positive, because training with counter-poised targets increases the need to selfregulate. Thus, we propose the following hypotheses:

#### 2.3.2. Transfer: Attention Test and Self-Reported Symptoms

Due to meta-analyses that find that NFB is efficacious for reduction of inattention (Arns et al., 2009), transfer of learning is expected to result in more improvement-over-baseline of the treatment group, compared with a control group, at the continuous performance test (CPT) Test Of Variables of Attention (T.O.V.A.) applied before and after training. Furthermore, those participants who perform better in baseline T.O.V.A. (lower scores), are expected to learn quicker during the NFB training.

Severity of subjective symptoms of ADHD/ADD should be reduced by the transfer of NFB learning to the ability to selfregulate. We also expect this effect to be dose-dependent, such that participants with better NFB performance (steeper positive slope) should present a higher rate of change in reported symptoms (steeper negative slope). Finally, the treatment group is expected to report fewer symptoms than the control group in the outcome measurement.

#### 2.4. Outcomes

Primary outcome measures include learning curve assessment, T.O.V.A., ASRS, and Digit span. These measures fall into two categories: (1) between-group comparison of NFB and WLC groups; and (2) within-subjects tests for treatment group. Primary measures are all comparative, truly experimental, and hypothesis-driven.

Secondary outcome measures include pre- and post-treatment vigilance measurement with an EEG protocol (Olbrich et al., 2012); also per-session self-report of circadian patterns, mood, excitement, effort and frustration; and the Pittsburgh Sleep Quality Index (PSQI) administered pre-, post- and at two intermediate points during treatment. These measures were taken to explore the additional research question of the relationship between sleep quality and NFB performance.

Additional methods taken to assess the quality of measurements include

#### 2.5. Sample Size

The power calculation of N = 60 was based on an estimated effect size for neurofeedback of 0.9, alpha at 0.05 and Power at 0.95 (for an independent samples t-test, estimate based on studies including Egner and Gruzelier, 2004; Rossiter, 2004a,b; Arns et al., 2009). Recent work has shown an effect size of 0.73 for a purely adult population (Mayer et al., 2012), but we can still maintain N = 60 by assuming power = 0.85, since this is equivalent to an phase 1 trial where the motivation is greater to minimize Type I error than Type II. This also contributes to our motivation to use a WLC along with the principle that NFB is closer in nature to a behavioral intervention than a drug trial, and thus double-blinding is unwarranted and unethical.

## 2.6. Randomization Procedure

We assign patients to test or control groups using a procedure that controls for selection bias, and known and unknown sources of external variance due to prognostic variables; the procedure is based on combined randomization and adaptive allocation.

Simple randomization prevents biased selection and meets the assumptions of uniform assignment probability made in standard inferential procedures. However, it does not guarantee equal group sizes and can lead to baseline imbalance in prognostic variables such as age, gender or disease severity. Equal group sizes

5http://www.measuringimpact.org/home.

can be obtained using blocking, while baseline imbalance can be helped by using blocking stratified over prognostic variables, although the number of variables which can be used is small. This limitation is surmounted by adaptive allocation, exemplified by minimization methods: these are not strictly random but allow tight control of balance of multiple prognostic variables (see e.g., Roberts and Torgerson, 1998 on methodological issues).

In their review of recommendations of assignment method, (Scott et al., 2002, p. 671) found a general support for minimization with a random element in smaller trials. Our approach follows: X% random blocking followed by 100-X% minimization (0<X<100). The algorithm is:

Assignment was balanced over age, sex, education, IQ, diagnosis (ADHD vs. ADD), comorbities from diagnosis, comorbities from administered scales, and ASRS subtype score, and tested between groups to show no statistically significant differences (see **Table 1** below). The same tests returned null when run after any change in relative group composition, due to e.g., drop-outs. Thus, groups did not differ in terms of symptom severity, diagnosis, IQ or demographic features at assignment.

Given that the initial allocation of patients is random, further non-random selections will also be random at the population level. Even if the minimization assignments cannot be considered random, the overall assignment retains a proportional amount

TABLE 1 | Demographic and clinical characteristics for NFB and WLC groups.

of randomness equivalent to X, similar to more complex biased-coin approaches. In this trial X was set equal to 50.

The most important caveat of the approach is that variables used in minimization must be included as covariates during analysis, to avoid potentially misleading results.

Technicians must be assigned to treatment and control group by recruitment on-demand, as the group treatment phases were separated in time.

#### 2.6.1. Allocation Concealment

Participants were randomly assigned by use of computerized algorithm detailed above, at one time, directly after all intake measurements and before the start of NFB treatment.

#### 2.6.2. Implementation

The allocation algorithm was designed and implemented by the lead researcher; the direct contact with participants for enrolment and assignment was handled by a technician working for the MCC clinic.

#### 2.6.3. Blinding

Due to the fact that the trial used waiting list for the control group, assessment was not blinded. Thus, after assignment, all participants and researchers/technicians had access to the assignment information. As mentioned above, since NFB is closer in nature to a behavioral intervention than a drug trial, doubleblinding would be unwarranted and unethical.

#### 2.7. Statistical Methods

Independent variables for group comparison include NFB training regime (TB vs. SMR), participant age and gender, and assigned group (treatment vs. WLC group). Dependent variables (DVs) for group comparison include the T.O.V.A. and the ASRS self-report.

T.O.V.A. variables, based on response times (RT) and error rates, include RT variability (RTV) indicating consistency; mean RT; Omission errors (OM) indicating inattention; Commission errors (COM) indicating impulsivity; as well as the D-prime score. D-prime is described as a measure of "perceptual sensitivity" and has been suggested as an index of arousal (Losier et al., 1996). T.O.V.A. variables are standardized in the analysis.

To evaluate the effect of NFB on T.O.V.A., we created five new variables by subtracting the baseline scores from the outcome measurement scores: RTV-change, mean RTchange, OM-change, COM-change, and D-prime-change. These T.O.V.A. change scores are subsequently compared for the treatment group and the WLC, using independent samples t-test. The Levene's test showed that the variances for the two groups were similar. Consequently, the independent samples t-test was run with equal variances assumed.

MANOVA was subsequently used to evaluate the effect of NFB training, compared to the WLC, in the outcome measures on the five dependent variables of T.O.V.A.

ASRS consists of 18 items tapping the frequency of recent DSM-IV criterion symptoms of adult ADHD, including a scale for Inattention (IA, max 36 points) and a scale for Hyperactivity-Impulsivity (HI, max 36 points). We calculated differences of scores between baseline and outcome measurements to create IA-change and HI-change scores. These difference scores are subsequently compared for the NFB and the control group, using independent samples t-test.

The Levene's test showed that the variances in IA-change were statistically significantly different in the two groups (F = 4.36, p < 0.05). As a result, the independent samples t-test for IA-change was run with equal variances not assumed. The opposite was the case for the variable HI-change.

In all our random coefficient models the intercept and slope are separately estimated for each participant. That is, the coefficients are estimated for each participant for the linear regression equation as follows: Score = Intercept + B (Session).

All participants were treated with NFB at a single center. All technicians were equivalently trained and capable; therefore clustering of participants per technician was based simply on scheduling logistics.

# 3. RESULTS

Results primarily describe the specific details of the intervention implementation. Also, as stated, a preliminary between-subjects comparison was performed after phase one to assess intervention efficacy under a conventional analysis model. Thus, we report the two straightforward pre- to post-treatment outcome measures which are comparable between-groups: T.O.V.A. and ASRS. The ambiguous results of this conventional approach, especially in an unblinded context, supports the motivation to extend the analysis with LC modeling. We therefore include these results in the protocol report because they constitute an informative part of the trial design going into the second phase.

#### 3.1. Participant Flow

Eighty-two adults were recruited through cooperating clinics Mental Capital Care, Neuromental and YTHS; also by newspaper advertisement and posting to online forums for the Helsinkibased ADHD society of Finland. Of this, 19 dropped out of the trial before completing the screening process, due to various issues.

Sixty-three participants were screened by a psychiatrist and a psychologist prior to the training, resulting in nine participants screened out of the trial due to one or more failures to meet the criteria. The remainder (n = 54) consisted of 29 females, 25 males, mean age 36 std.dev. 10 years, with 44 ADHD and 10 ADD diagnoses.

Participants were split equally between treatment (n = 27) and control groups (n = 27); however two switched from treatment group (final n = 25) to control group (final n = 29) for personal reasons. From these assignments, eight dropped out from the WLC group (including one participant whose pre-test measurement data was then deleted by request), and two from the treatment group. Thus, 23 treatment group, and 21 WLC group cases (total n = 44) were available for analysis. The trial progression is shown in detail in **Figure 1**.

Out of those who completed NFB, five participants did not complete treatment on the pre-defined schedule, of which three exceeded by more than a week. Delays were due to personal reasons, causing a number of cancelations of scheduled sessions, which is a regularly observed phenomenon in this diagnostic group.

#### 3.1.1. Implementation of Intervention

In practice, NFB training consisted of ∼40 sessions (range: 38– 41) during 2–4 months. There was a mid-training pause of nominally 2 weeks. Patients came to the sessions 2–5 times a week. One session lasted ∼1 h, subdivided into self-report of mood, excitement, hours slept and hours awake; electrode attachment; baseline measurement; 5–7 units of 5 min NFB trials; and debrief including self-report of effort and frustration. During each session, patients played different NFB "game" trials during which they got immediate visual reinforcement for classifiermatching states in their EEG. The scores per game trial are baseline-adjusted and averaged per session to form characteristic LCs. The content and purpose of the training sessions followed a phased timeline:

#### 3.1.2. Recruitment

As shown in **Figure 1**, participants were recruited in May/June 2012, with intake measurements during July/August 2012. Randomization took place in early September; treatment began September 17, 2012. Outtake measurements ran from April until August 2013.

Follow-up measurements are planned for start of 2016.

# 3.2. Baseline Data

The participants who began the trial (n = 54, 29 females) had mean age 36 years (std.dev. 10 years), with 44 ADHD and 10 ADD diagnoses. The characteristics per group are shown below in **Table 1**.

All intervention sessions were performed at the Mental Capital Care (MCC) clinic premises in Helsinki, which met all requirements described above. Technicians were recruited to administer the NFB from IBS and MCC; they were trained at UOH over a 3 month period; four of the team also attended the Biofeedback Certification International Alliance (BCIA) accredited introductory course at the Brainclinics education and treatment center, Netherlands.

#### 3.2.1. Numbers Analyzed

The analysis included 23 participants in the treatment group, and 21 participants in the WLC group. Given that dropouts were not analyzed, this analysis was not by intention-to-treat.

#### 3.3. Outcomes and Estimation

For H2a we find no support after analysis of the five T.O.V.A. indexes; **Table 2** shows their mean differences between baseline and post-training. Changes in these variables were not significantly different for the NFB group than for the WLC. Results of the MANOVA at the outcome measurement revealed that on the Wilks' Lambda the difference in means between the NFB and WLC did not reach significance [F(5, 38) = 0.45, p >0.05]. Thus, NFB training had no significant effect on the 5 indexes of T.O.V.A. at the end of the intervention.

**Table 3** presents the mean difference of the two indexes between baseline and post-training and the results of the independent sample t-tests comparing IA-change and HI-change between NFB group and WLC. The NFB group presented a higher reduction of inattention symptoms than the WLC t(36.03) = −2.14, p < 0.05. Similarly, while the NFB group presented a reduction of HI symptoms from baseline to posttraining, the WLC presented an increase in HI symptoms t(44) = −2.42, p < 0.05. Thus, we find statistically significant support for H3c. That is, the treatment group reported greater improvements in ADHD/ADD symptoms than the WLC.

A more detailed analysis concerning the rest of the hypotheses will follow in a separate paper. As treating the hypotheses H1a-b, H2b, and H3a-b requires substantial additional methodological reporting, addressing them does not fit the scope of a clinical trial report.

#### 3.3.1. Adverse Effects

No adverse effects were observed in the treatment group. Further investigation of this question is planned for follow-up, using the state-oriented self-report items described above.

#### 4. DISCUSSION

#### 4.1. Interpretation

Regarding the relationship between NFB learning and performance in the continuous performance test, H2a proposed that the NFB group will achieve better T.O.V.A. performance, and improve more after training, than the WLC. Results of this study did not find evidence for such transfer. Patients participating in the NFB training did not perform better than WLC on the 5 indexes of T.O.V.A. in the outcome measurement. This result can be interpreted in at least two ways. On the one hand, it can be a sign of the all too common problem of transfer of training (Green and Bavelier, 2012).

Lack of transfer is one of the most important of the several key obstacles pertaining to the effect of NFB trainings (Gazzaniga, 2009, p. 94). Because brain plasticity is highly task specific, training in a specific task shows little or no improvement on related tasks. On the other hand, the results of this study can mean that NF learning bears no relationship to performance on any indexes of the T.O.V.A. test. This would contradict the findings of Losier et al. (1996) who considered the D-prime index of T.O.V.A. a consensus index of arousal, which is, in turn, assumed to be a manifestation of excessive slow wave brain waves in ADHD patients (Barry et al., 2003).

TABLE 2 | Groups statistics for RTV-change, mean RT-change, OM-change, COM-change and D-prime-change scores from baseline to outcome.

TABLE 3 | Groups statistics for IA-change and HI-change scores.

H3c suggested that the NFB group will report greater improvements in ADHD symptoms than the WLC. Results show the change of IA was significant. Patients did perceive a reduction of inattention symptoms over the course of the training. Furthermore, this perceived reduction of inattention symptoms differed significantly from the perceived reduction of inattention symptoms of the control group. This supports the meta-analysis by Arns et al. (2009) concluding that NFB has large effect sizes on inattention. In the index of HI, no negative linear trend was found. Interestingly however, patients in the NFB group did perceive a significantly larger reduction of these symptoms over the course of the training than the control group.

It might be that the training indeed resulted in some reduction of IA and HI symptoms. This interpretation gets support from theories claiming that ADHD is, in effect, pathology of executive functions that cannot be tapped by neurocognitive tests, but can instead be measured by self-reported questionnaires (Rabbitt, 1997; Brown, 2009, pp. 81–116). Alternatively, the results can also be interpreted as an example of the so called Hawthorne effect (Green and Bavelier, 2012). Establishing the presence of experience-dependent learning effects is not always straightforward. It is well documented, that individuals who take an active interest in their performance tend to improve more, or evaluate their improvement more positively. The Hawthorne effect can lead to powerful subjective improvements that have little to do with the specific cognitive training regimen being studied reflecting motivational factors instead.

If the training caused the decreased symptoms, a higher rate of learning should have a relationship to the rate of ADHD symptom decrease under the training. Therefore further analyses of this trial will examine the relationship between NFB LCs and the trend of self-reported ADHD symptoms.

Also, the different learning performance levels across the group should reflect different long term effects, to be measured during the second phase in a within-subjects analysis. This contrasts with the effect from the interaction-derived placebo which is relatively constant for all participants (per group), and presumably "fades away" quickly after the phase one intervention. Thus, second phase measurements will be analyzed with respect to NFB LCs.

Though this is a relatively small study, we believe the analysis of learning curve questions is a novel and useful contribution. As noted by Arns (personal communication, 2012):

"In any Neurofeedback study it is very important to track and have an indication of 'learning.' If neurofeedback fails to demonstrate a clinical effect and there is no indication that learning actually took place, one can't draw any conclusions about neurofeedback. In an analogy, if one employs operant conditioning to [teach] a rat to press a lever, and the rat does not learn to press the lever, then it is incorrect to conclude that 'operant conditioning' does not work. This means that maybe the operant conditioning procedure was not implemented effectively. The same applies to neurofeedback, and this is further illustrated by the study from Roger DeBeuss. His study employed suboptimal parameters e.g., auto-thresholding, 'game' feedback and an 'unconventional' training regime (engagement index) making it likely harder to learn. On the group level they found no effects of neurofeedback in ADHD, however, when separating learners from non-learners based on session data they did find an effect."

Questions of efficacy on the other hand are still a matter of controversy in the literature. WLC controls are not accepted as sufficient evidence by some. Double blind control through sham NFB was not chosen for this study because of the discussed lack of general understanding of ADHD. This lack, combined with the extremely contingent nature of NFB which depends heavily on non-specific aspects of treatment, implies that even if a double blind RCT showed large effect for NFB the causal mechanisms would still not be clear. A true resolution to this issue is probably only possible by running the kind of large sham NFB RCT called for by others; we choose instead to side-step this debate and focus on questions of internal comparisons within the method.

There is no blinding in this open-label WLC study, except at random assignment to groups. Other control paradigms may be preferred in pharmacological interventions; however there are a number of arguments in support of WLC, in the context of NFB. The WLC is a minimum viable control for non-specific effects of history, maturation, repeated testing, instrument drift, statistical regression, selection bias, and population inhomogeneity effects (Mohr et al., 2009). There is no control for non-specific or placebo effects; however this is still a valid experimental control design. Among other things, WLC controls for expectation and attention (Hawthorne) effects, whereby the notion that at some future point treatment will be provided (and life will improve) is by itself able to produce improvement. Additionally, longitudinal (rather than parallel) designs control for maturation, regression to the mean, instrument drift and practice effects; also time threats to validity (the same effect occurring 2 years in a row in different samples rules out external non-seasonal temporal causes for the effect. Seasonal causes with a WLC group should be ruled out by staggered application i.e., 2nd treatment starting in spring).

Technicians can be considered equally skilled and expert. All began as neurofeedback novices before the trial. Qualification history was varied. Due to availability, only some began their training with attendance at the Biofeedback Certification International Alliance (BCIA)-accredited introductory course at the Brainclinics education and treatment center, Netherlands. However, all five then shared 3 months training at UoH premises, including extensive peer review work, which helped to disseminate and pool the knowledge across the group.

# 4.2. Generalizability

External validity asks the question of "generalizability": to what populations, settings, treatment variables and measurement variables can this effect be generalized? While the question of external validity is never completely answerable, it is of particular interest for intervention research (Campbell et al., 1966, pp. 171–246). Campbell et al. (1966) note that there is a recurrent reluctance among researchers to accept Hume's truism that induction or generalization is never fully justified logically. While the problems of internal validity are solvable within the limits of the logic of probability statistics, the problems of external validity are not logically solvable in any neat, conclusive way (Campbell et al., 1966, p. 17). Generalization always involves extrapolation into a realm not represented in one's sample. Here, the issue of sample bias is of importance. If an experimental study is conducted with voluntary patients from a given district, they might have characteristics that cause the experimental treatment to be more effective than it would be in other populations. However, for ethical reasons, intervention studies are impossible to conduct without the informed consent of the research subjects. It is obvious, that a "true" experimental design is in practice impossible with a fully representative sample of a given country, let alone all ADHD patients in the world. It must be emphasized that the results of an experiment "probe" but do not "prove" a theory. An adequate hypothesis is one that has repeatedly survived such probing, but it may always be displaced by a new probe. Many findings in experimental psychology gain generalizability not through the nature of the setting in which they occurred, but through their ability to establish a theory of basic mental processes that are implicated in many tasks.

## 4.3. Overall Evidence

After preliminary analysis, the trial did not find evidence for a transfer of learning that was the intended benefit of the intervention. Since the intervention's goal is symptomatic improvement outside the laboratory, the results underline how the transfer-problem limits the potential benefits. Patients did perceive a reduction of Inattention symptoms over the course of training, yet this change was not reflected in better performance in the continuous performance test (T.O.V.A.).

We propose that in the absence of any other evidence, one should consider these self-report results as due to placebo by default. The improvement in self-reported symptoms might not be specifically due to NFB, but due to employment of goaloriented attention in general. At present, we can not rule out the possibility that the individuals would also report improvement if all other factors were held equal but NFB was swapped for some other exercise that requires concentration. That is, our preliminary results did not support the effectiveness of NFB in alleviating the symptoms of AHDH/ADD.

Nevertheless, keeping in mind the core aim of studying mechanisms and models of NFB, the CENT trial is on track to provide the necessary evidence. As Seidman (2006) suggest, an adequate neuropsychological model of ADHD should utilize measures from multiple domains to be able to encompass

# REFERENCES

subtypes and multiple deficits. Combining neuropsychological, neurophysiological and behavioral measures, we aim toward an evaluation of structure-function relationships in NFB treatment for adult ADHD.

#### AUTHOR CONTRIBUTIONS

BC designed and implemented the study and wrote the draft text; EH conducted the statistical analyses and contributed to the draft; KJ and LK contributed to the study design and implementation, and the draft; CK contributed to the study design and the draft.

# ACKNOWLEDGMENTS

The authors wish to thank Svetlana Kirjanen, Mona Moisala, Marko Repo, Hanna Björkstrand, Tanja Hyttinen, Jari Torniainen, Teemu Itkonen, Markus Kivikangas for their part in running the trial; also Laura Hokkanen and Jari Lipsanen for assistance with the work of EH. Partly funded by Finnish science agency TEKES, project #440078.

Liebowitz, M. R. (1992). Dissociative experiences scale. Am. J. Psychiatry 149, 719.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2016 Cowley, Holmström, Juurmaa, Kovarskis and Krause. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Beware: Recruitment of Muscle Activity by the EEG-Neurofeedback Trainings of High Frequencies

Katarzyna Paluch1 \* † , Katarzyna Jurewicz 1 \* † , Jacek Rogala1 , Rafał Krauz 2 , Marta Szczypi´nska3 , Mirosław Mikicin3 , Andrzej Wróbel 1 and Ewa Kublik 1

1Department of Neurophysiology, Nencki Institute of Experimental Biology of Polish Academy of Science, Warsaw, Poland, 2Centre for Physical Education and Sport, Military University of Technology, Warsaw, Poland, 3Department of Physical Education, University of Physical Education, Warsaw, Poland

EEG-neurofeedback (NFB) became a very popular method aimed at improving cognitive and behavioral performance. However, the EMG frequency spectrum overlies the higher EEG oscillations and the NFB trainings focusing on these frequencies is hindered by the problem of EMG load in the information fed back to the subjects. In such a complex signal, it is highly probable that the most controllable component will form the basis for operant conditioning. This might cause different effects in the case of various training protocols and therefore needs to be carefully assessed before designing training protocols and algorithms. In the current experiment a group of healthy adults (n = 14) was trained by professional trainers to up-regulate their beta1 (15–22 Hz) band for eight sessions. The control group (n = 18) underwent the same training regime but without rewards for increasing beta. In half of the participants trained to up-regulate beta1 band (n = 7) a systematic increase in tonic EMG activity was identified offline, implying that muscle activity became a foundation for reinforcement in the trainings. The remaining participants did not present any specific increase of the trained beta1 band amplitude. The training was perceived effective by both trainers and the trainees in all groups. These results indicate the necessity of proper control of muscle activity as a requirement for the genuine EEG-NFB training, especially in protocols that do not aim at the participants' relaxation. The specificity of the information fed back to the participants should be of highest interest to all therapists and researchers, as it might irreversibly alter the results of the training.

Keywords: artifacts, attention, beta rhythm, biofeedback, muscle control, placebo

# INTRODUCTION

In the last two decades, EEG-based neurofeedback (EEG-NFB) received vast popularity in clinical and paramedical practice, even though the therapeutic usage of this method was precariously ahead of the careful, systematic examination of its physiological mechanisms, confounding factors and possible side effects.

The method belongs to a broader category of biofeedback techniques aimed at altering various physiological parameters such as heart rate (ECG-feedback), muscle tension (EMG-feedback)

#### Edited by:

Soledad Ballesteros, National University of Distance Education, Spain

#### Reviewed by:

Felix Darvas, University of Washington, USA José Luis Ulloa, Ghent University, Belgium

#### \*Correspondence:

Katarzyna Paluch<EMAIL_ADDRESS>Katarzyna Jurewicz<EMAIL_ADDRESS>
†These authors have contributed equally to this work.

Received: 14 September 2016 Accepted: 27 February 2017 Published: 20 March 2017

#### Citation:

Paluch K, Jurewicz K, Rogala J, Krauz R, Szczypi ´nska M, Mikicin M, Wróbel A and Kublik E (2017) Beware: Recruitment of Muscle Activity by the EEG-Neurofeedback Trainings of High Frequencies. Front. Hum. Neurosci. 11:119. doi: 10.3389/fnhum.2017.00119

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and others. The trainings are based on the assumption that one can learn to change her/his brain physiological activity in a chosen oscillatory frequency by virtue of continuous feedback about its amplitude. On the contrary to research on brain computer interfaces (BCI) which concentrates on finding easily detectable and modifiable signals that can be reliably used by machine control algorithms, the EEG-NFB training is directed toward lasting changes of brain activity and behavioral improvement. The trainings aim to induce systematic increases/decreases of predefined specific EEG frequencies reflecting particular cognitive or behavioral functions.

The EEG-NFB has been tested as a treatment in a vast domain of neurological and psychiatric disorders, e.g., epilepsy (Sterman and Friar, 1972; Kotchoubey et al., 2001), attention deficit hyperactivity disorder (ADHD; Kaiser and Othmer, 2000; Fuchs et al., 2003; Kropotov et al., 2007), schizophrenia (Gruzelier et al., 1999; Surmeli et al., 2012) and even in traumatic brain injury (TBI) and stroke rehabilitation (for the review of clinical applications, see Yucha and Montgomery, 2008). In healthy subjects the EEG-NFB has been applied expecting behavioral and/or cognitive improvements (Arns et al., 2008; Reiner et al., 2014) and as a supportive training of cognitive performance in the elderly (Becerra et al., 2012; Staufenbiel et al., 2014).

 This large diversity of protocols (i.e., sets of frequency bands used to up- or down-regulate their amplitudes or their ratios) resulted from a belief that each frequency range is related to some specific cognitive functions. Even though instances of such frequency-to-function mapping have been documented (Wróbel, 2000, 2014; Wang, 2010; Anguera et al., 2013), their complex interactions do not allow for simplifying generalizations and require further investigations. Among others, the beta band has been posited to be an attention carrier (Wróbel, 2000, 2014), with specific, local increases of amplitude during attentional tasks positively correlating with correct performance in animals and humans (Bekisz and Wróbel, 1993; Buschman and Miller, 2007; Wróbel et al., 2007; Kami´nski et al., 2012; Gola et al., 2013). The up-regulation of this band has been applied in more complex EEG-NFB training protocols as a supplementary treatment in ADHD (e.g., Lévesque et al., 2006; Leins et al., 2007) and as skill enhancement for sportsmen and the elderly (e.g., Rostami et al., 2012; Staufenbiel et al., 2014).

Research focusing on beta and gamma bands confronts the problem of EEG contamination by muscle activity. Electromyographic activity recorded on the surface of the skin is composed of high frequencies with most of the power concentrating between 20 Hz and 150 Hz (Criswell, 2011). In consequence, muscles located on the head (e.g., temporal, occipitofrontal and auricular muscles) or even more distally can interfere with the EEG, sometimes constituting a majority of the power in the higher frequencies (Goncharova et al., 2003; Whitham et al., 2007) and influencing most of the electrodes on the scalp (Goncharova et al., 2003; Yilmaz et al., 2014). The relation between these signals is further complicated by the fact that facial EMG was shown to be sensitive to numerous cognitive and affective processes, including cognitive load (Waterink and van Boxtel, 1994; Whitham et al., 2008). Muscle interference has been widely discussed in the context of EEG and EMG data analysis (for review see McMenamin et al., 2011; Muthukumaraswamy, 2013) but is not sufficiently recognized and controlled in EEG-NFB trainings (see ''Discussion'' Section and Enriquez-Geppert et al., 2017). The EEG signal analyzed offline can be iteratively examined and cleaned of any obscuring components. However, in the EEG-NFB the signal is analyzed online and transformed into stimuli immediately fed back to a trained person. All undetected artifacts modify the feedback signal. This might cause different effects in the case of various training protocols and therefore needs to be carefully considered while designing training protocols and algorithms.

Here we report a particular instance of this problem concerning the NFB up-regulation of the beta band. Since there is vast research on clinical and normal population reporting no control or one with doubtful effectiveness (e.g., Leins et al., 2007; Gevensleben et al., 2009a,b; Keizer et al., 2010a,b; Logemann et al., 2010; Meisel et al., 2014; see ''Discussion'' Section), we conducted an experiment to assess the possible impact of muscle activity on EEG-NFB results. We applied the beta up-regulation set-up, commonly used as training aimed at improving attention (Egner and Gruzelier, 2001; Vernon et al., 2003; Egner et al., 2004; Logemann et al., 2010; Ghaziri et al., 2013). Healthy, young participants were trained to voluntarily increase the amplitude of beta1 band oscillations (15–22 Hz) recorded from the leads overlying the areas of the frontoparietal attention network. Examination of the raw signal revealed in a subgroup of participants a substantial muscle employment which increased systematically in the course of the session, mimicking the expected increase in the beta1 band. If the muscle related effects had gone unnoticed, the conclusion of our study would have been falsely positive stating a successful upregulation of the beta1 band. We discuss the need for proper muscle control for a reliable NFB training in the light of the current EEG-NFB literature.

# MATERIALS AND METHODS

#### Participants

Thirty-two male healthy university students, age m = 21.97 ± 1.88 years (mean ± standard deviation), were recruited for the experiment. The experiments were approved by the local ethics committee (Bioethical Committee at the Military Institute of Hygiene and Epidemiology). All subjects were informed about the study and gave their written informed consent for participation in the experiment in accordance with the Declaration of Helsinki.

# The EEG-Neurofeedback Training

The subjects were randomly assigned to one of the three training groups: beta plus (B+), aimed at increasing the amplitude of beta1 (15–22 Hz) oscillations (n = 14), beta minus (B−) dedicated to down-regulation of the beta1 oscillatory activity (n = 6) and sham (SH) group, receiving pseudo-feedback (generated by a computer algorithm), unrelated to the brain's EEG signals (n = 12). The participants were unaware of their group affiliation and uninformed about the existence of the sham group to prevent loss of motivation.

The training sessions were performed using a customized version of the commercial EEG DigiTrack Biofeedback system (ELMIKO MEDICAL Sp. z o. o.). Each participant had a personal code, which was recognized by the program and started groupdependent feedback protocol. At the user (trainer) level, the program displayed set-ups only for two groups: beta plus and beta minus. Sham protocols were run under facade of these set-ups (half as B+ and the other half as B−). The trainings were conducted by hired professional NFB trainers. In order to reduce possible nonspecific effects trainers were instructed not to additionally motivate the trainees. Over a period of 1–2 months the subjects underwent eight training sessions (one to two trainings per week). During the session subjects were seated in a chair in front of a 1700 computer LCD screen (∼70 cm from the screen). Each session consisted of 10 blocks of 3 min duration each. The session started after mounting the EEG electrodes with a short (ca. 2 min) resting period in order to accustom the participants with the training situations and screen the control sample of the EEG signal.

EEG was recorded from F3, F4, P3 and P4 sites in 10–20 standard, with linked ears as a reference and ground electrode placed at the Pz. Thus, the electrodes were positioned over the frontoparietal attention network nodes (Gross et al., 2004; Donner et al., 2007; Siegel et al., 2008). The signal was sampled at 250 Hz and band-pass filtered between 0.16 Hz and 70 Hz, with a notch filter at 50 Hz. A fast Fourier transform (FFT) spectrogram was computed for each electrode. The feedback parameter was obtained by averaging the FFT amplitudes over the beta1 range and across the electrodes. The FFT window of 2.07 s (512 point, giving 0.49 Hz resolution) was sliding with 92% or 77% overlap and, accordingly, the amplitude values presented to the trainer and used for feedback were updated with 200 or 500 ms delay. Windows overlap and the feedback delay varied between subjects (due to two software versions used in the study) but were constant for each person and randomly distributed among the experimental groups. There was no difference in results of participants trained with these two settings.

The training display consisted of a shooting target presented in the background and four green dots moving inwards and outwards along vertical and horizontal axes (**Figure 1**). The feedback information about the amplitude of beta1, was provided by the synchronized movement of the dots in B+ and B− groups. In the sham group a predefined algorithm controlled the movement of the dots. When the amplitude changed in the intended direction (towards the threshold value set manually by the trainers) all dots moved inwards. The subject's goal was to make the green dots meet in the center. To boost the participants' motivation and to make the training more involving additional reinforcements were provided. When the beta1 amplitude reached 43% of the threshold, the display was complemented with black rings within the high-scored area of the shooting target. When the beta1 amplitude reached 75% of the threshold value, a red ring in the center of the shooting target was presented, signaling achievement of the goal. The threshold defining the required value of the beta1 band amplitude was adjusted manually by the trainers during the session to provide a relatively constant rate of reward, thus encouraging the subjects to continuously improve their performance.

#### Processing of the EEG Data

Raw EEG data were exported from the DigiTrack environment to the European Data Format (EDF) and further analyzed using the EEGLAB software (Delorme and Makeig, 2004) and self-written MATLAB scripts. The off-line analysis involved similar preprocessing of the data as the online feedback computation, to obtain the same frequency bands to those produced by the EEG-NFB apparatus. The continuous data recorded during training session were mean corrected and filtered to remove frequencies lower than 0.5 Hz and higher than 70 Hz. Notch filtering was applied at 50 Hz. The signal was split into 1 s epochs, which in turn were searched for artifacts with the EEGLAB function pop\_autorej. An epoch was rejected from all the channels if any data point in this epoch exceeded 5 standard deviations from the amplitude of the signal on any of the channels. The algorithm proceeded iteratively—if the number of epochs classified for exclusion exceeded 5% of the data, the procedure was repeated with a more liberal exclusion criterion (increased by 0.5 standard deviation). Furthermore, to remove muscle artifacts, for every subject we removed whole training blocks, in which higher frequencies (22–45 Hz, referred to in DigiTrack software as beta2 band) diverged by more than 3 standard deviations from the individual mean of that participant. The procedure ran iteratively until no such cases were found. In effect, 3.05% of the data was removed, including three full sessions (belonging to two subjects). The FFT analysis was computed separately for each 3 min training block with a sliding Hanning window 512 points-long with a 92% overlap.

The visual inspection of the raw signal and the FFT spectra (performed after all cleaning steps described above) revealed, that the signals of some participants were dominated by frequencies above 15 Hz (**Figure 2**). Since such oscillations constituted the majority of some participants' signal, cleaning algorithms relaying on signal distribution parameters e.g., mean and standard deviation were unable to detect and reject them during offline automatic data processing. These signals were characterized by long sweeps of sharp high frequency oscillations characteristic for EMG (Criswell, 2011). In these cases, a typical logarithmic-like shape of the FFT curve was substantially distorted by an elevation spanning from higher to lower parts of the spectrum.

Therefore, we asked three independent judges to visually inspect the raw signals and the FFT spectra from all blocks and sessions of each participant and to classify the sessions as contaminated by muscle activity if FFT spectrum was distorted above 15 Hz in the majority of blocks (**Figure 2**; Criswell, 2011). The judges listed the participants who exhibited such a pattern in more than half of their sessions as muscle employing ones.

To confirm that it is possible to distinguish muscle-employing participants from the whole sample in an automated approach we applied two additional methods of classification: k-means clustering and logistic regression. For both these methods we used solely the amplitudes of beta2 as the bases of classification.

When applying the k-means method we asked for two clusters, to prove that our division into muscle employers and others is the most prevalent pattern in the data. The analysis was performed with the use of beta2 amplitudes from all

available recordings (10 blocks × 8 sessions for each subject, 3.05% of blocks were missing, due to previous data cleaning and were substituted with the average of a given participant). The algorithm was set to minimize absolute deviations within clusters by calculating the median along predefined dimensions (Manhattan distance).

The logistic regression was applied as a supervised method of data classification. The binary classifier (muscleemploying/other) was guided by the classification made by independent judges. The mean beta2 amplitude from all available recordings for each participant constituted the predictor value. The regression line, fitted to the data, quantified the relationship between beta2 amplitude and the probability of belonging to one of the two categories. The decision criterion was established at a probability of 0.5.

In addition to the trained beta1 band (15–22 Hz), we reported on the alpha (8–12 Hz) and beta2 (22–45 Hz) flanking bands, as they are capable of showing potential specificity of the training effects.

#### Statistical Analyses