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**128**

**Author details**

Thailand

Thailand

and Chaiartid Inkham2,4\*

University, Chiang Mai, Thailand

University of Agriculture, Japan

provided the original work is properly cited.

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[2] White P. Long-distance transport in the xylem and phloem. In: Marschner P. editors. Marschner's Mineral Nutrition of Higher Plants. 3rd ed. Berlin: Elsevier; 2012. p. 49-70. doi: 10.1016/ B978-0-12-384905-2.00003-0

[3] Avice JC, Etienne P. Leaf senescence and nitrogen remobilization efficiency in oilseed rape (*Brassica napus* L.). J. Exp. Bot. 2014; 65: 3813-3824. DOI: 10.1093/jxb/eru177

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[5] Ruamrungsri S, Apavatjrut P. Effect of nutrient deficiency on growth and development of *Curcuma alismatifolia* Gagnep. In: Proceedings of the 3rd Symposium on the Family Zingiberaceae; 7-12 July 2003; Khon Kaen. Thailand; 2003. p. 98-104.

[6] Ohtake N, Ruamrungsri S, Ito S, Sueyoshi K, Ohyama T Apavatjrut, P. Effect of nitrogen supply on nitrogen and carbohydrate constituent accumulation in rhizomes and storage roots of *Curcuma alismatifolia* Gagnep. Soil Sci. Plant Nutr. 2006; 52: 711-716. https://doi.org/ 10.1111/j.1747-0765.2006.00094.x

[7] Nain S, Beniwal BS, Dalal RPS, Sheoran S. Effect of nitrogen and phosphorus application on growth, flowering and yield of African marigold (*Tagetes erecta* L.) under semi-arid conditions of Haryana. Indian Journal of Ecology. 2016; 43 (Special Issue-2): 645-649.

[8] Ahirwar MK, Ahirwar K, Shukla M. Effect of plant densities, nitrogen and

phosphorus levels on growth, yield and quality of African marigold. Annals of Plant Soil Research. 2012; 14(2): 153-155.

[9] Król B. The effect of different nitrogen fertilization rates on yield and quality of marigold (*Calendula officinalis* L. 'TOKAJ') raw material. Acta Agrobotanica. 2011; 64 (3): 29-34. DOI: https://doi.org/10.5586/aa.2011.027

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[11] Marschner P. Marschner's mineral Nutrition of Higher Plants. Academic Press, Elsevier: Amsterdam; 2012. 651 p.

[12] Anuwong C, Ohtake N, Sueyoshi K, Ruamrungsri S, Ohyama T. Characterisation of proteins in the storage organ of *Curcuma alismatifolia* Gagnep. Journal of horticultural Science & biotechnology. 2014; 89(5): 501-507. DOI: 10.1080/14620316.2014.11513112

[13] Ohtake N, Ruamrungsri S, Ito S, Sueyoshi K, Ohyama T, Apavatjrut P. Effect of nitrogen supply and carbohydrate constituent accumulation in rhizomes and storage roots of *Curcuma alismatifolia* Gagnep. Soil Science and Plant Nutrition. 2006; 52: 711-716. https://doi.org/10.1111/ j.1747-0765.2006.00094.x

[14] Ruamrungsri S, Ohtake N, Sueyoshi K, Suwanthada C, Apavatjrut P, Ohyama T. Changes in nitrogenous compounds, carbohydrates and abscisic acid in *Curcuma alismatifolia* Gagnep. During dormancy. J of Horticultural Science & Biotechnology. 2001; 76 (1): 48-51. DOI: 10.1080/14620316.2001. 11511325

[15] Khuankaew T, Ruamrungsri S, Ito S, Sato T, Ohtake N, Sueyoshi K, Ohyama T. Assimilation and translocation of nitrogen and carbon in *Curcuma alismatifolia* Gagnep. Plant Biol. 2009; 12: 414-423. DOI: 10.1111/ j.1438-8677.2009.00229.x

[16] Anuwong C, Ohyama T, Sueyoshi K, Ohtake N, Ruamrungsri S. Anatomical investigation of root and leaf of *Curcuma alismatifolia* Gagnep. Cv Chiang Mai Pink affecting N uptake. In: Proceedings of international graduate research conference Chiang Mai University. Chiang Mai, Thailand: 2014. p. 157-162.

[17] Marschner H. Mineral Nutrition of Higher Plants. Academic Press. Amsterdam; 1986. 674 p.

[18] Anuwong C, Ohyama T, Sueyoshi K, Ohtake N, Sato T, Ruamrungsri S. Uptake and translocation of nitrogen in Patumma (*Curcuma alismatifolia*) from foliar spray or root application. Journal of Plant Nutrition. 2017; 40(8): 1204- 1212. DOI: 10.1080/01904167.2016. 1264597

[19] Khuankaew T, Tanabata S, Yamamoto M, Ishikawa S, Tsutsumi K, Ohtake N, Sueyoshi K, Ohyama T, Sato T, Anuwong C, Ruamrungsri S. Temperature affects N and C assimilation and translocation in *Curcuma alismatifolia* Gagnep., The Journal of Horticultural Science and Biotechnology. 2014; 89(3): 287-292. DOI: 10.1080/14620316.2014.11513081

[20] Hongpakdee P, Ohtake N, Sueyoshi K, Ohyama T, S Ruamrungsri. Effects of low night temperature and short-day length on some phytohormones and nutrient status in *Curcuma alismatifolia* Gagnep. Thai journal of Agricultural Science. 2010; 43(3): 163-173.

[21] Inkham C, Sueyoshi K, Ohtake N, Ohyama T, Ruamrungsri S. Effect of temperature and nitrogen sources on growth and nitrogen assimilation of *Curcuma alismatifolia* Gagnep. Thai

journal of Agricultural Science. 2011; 44(3): 145-153.

[22] Ruantip T, Ruamrungsri S. Effects of nitrogen fertilizer levels on growth, flower and rhizome qualities of *Curcuma alismatifolia* Gagnep. In: Proceeding of the 2nd CMU graduate research conference. 26 November 2010. Graduate school. Chiang Mai University; 2010. p. 4.

[23] Ruamrungsri S, Hamtisong N, Choonluchanon S. Selection of endophytic bacteria from *Curcuma alismatifolia* for N2 fixation and IAA synthesis. In: Proceedings of the 3rd International meeting for the development of IPM in Asia and Africa, 7-9 December 2009; Bandar Lampung, Indonesia.

[24] Thepsukhon A, Choonluchanon S, Tajima S, Nomura M, Ruamrungsri S. Identification of endophytic bacteria associated with n2 fixation and indole acetic acid synthesis as growth promoters in *Curcuma alismatifolia* Gagnep. Journal of Plant Nutrition. 2013; 36(9): 1424-1438. DOI: 10.1080/01904167.2013.793712

[25] Ruamrungsri S, Ohyama T, Konno T, Ikarashi T. Deficiency of N, P, K, Ca, Mg or Fe mineral nutrients in *Narcissus* cv. 'Garden Giant'. Soil Science and Plant Nutrition. 1996; 42(4): 809-820. https:// doi.org/10.1080/00380768.1996.10416628

[26] Ruamrungsri S, Ohyama T, Ikarashi T. Nutrients, free amino acid and sugar contents in *Narcissus* roots affected by N, P, K deficiency during winter. Soil Science and Plant Nutrition. 1996; 42(4): 765-771.

[27] Ruamrungsri S, Ruamrungsri S, Ikarashi T, Ohyama T. Uptake, translocation and fractionation of nitrogen in *Narcissus* organs by using 15N. Acta Horticulturae. 1997; 430: 73-78. https://doi.org/10.1080/00380768 .1996.10416624

**131**

2010.878.41

*Nitrogen in Flowers*

0.11511227

1997. 370 p.

*DOI: http://dx.doi.org/10.5772/intechopen.98273*

[29] Hew CS, Yong JWH. The physiology of tropical orchids in relation to the industry. World Scientific: New Jersey;

[30] Panjama K, Ohyama T, Ohtake N, Sato T, Potapohn N, Sueyoshi K, Ruamrungsri S. Identifying N sources that affect N uptake and assimilation in *Vanda* hybrid using 15N tracers. Horticulture, Environment, and Biotechnology. 2018; 59(6): 805-813. DOI: 10.1007/s13580-018-0071-6

[31] Ruamrungsri S, Khuankaew T, T Ohyama. Nitrogen sources and its uptake in *Dendrobium* orchid by 15N Tracer study. Acta Horticulturae. 2014; 1025: 207-212. DOI: 10.17660/ActaHortic.2014.1025.30

[32] Ruamrungsri S, Samanit P,

Pornsawatchai T, Potapohn N, Fukai S. Effect of fertilizer application on nutritional concentrations and flower quality of Phalaenopsis hybrid. Acta Horticulturae. 2007; 755: 495-498. DOI: 10.17660/ActaHortic.2007.755.68

[33] Wang YT. Phalaenopsis mineral nutrition. Acta Horticulturae. 2010; 878: 321-334. DOI: 10.17660/ActaHortic.

[34] Tunprasas W, Ruamrungsri S. Effect of nitrogen on cut rose quality in potted production. J of Agriculture. Chiang Mai university. 2016; 32(3): 339-345.

[28] Ruamrungsri S, Ruamrungsri S, Ikarashi T, Ohyama T. Ammonium and nitrate assimilation in Narcissus roots. J. Hortic. Sci. Biotech. 2000; 75: 223-227. https://doi.org/10.1080/14620316.200

*Nitrogen in Flowers DOI: http://dx.doi.org/10.5772/intechopen.98273*

*Nitrogen in Agriculture - Physiological, Agricultural and Ecological Aspects*

journal of Agricultural Science. 2011;

[22] Ruantip T, Ruamrungsri S. Effects of nitrogen fertilizer levels on growth, flower and rhizome qualities of *Curcuma alismatifolia* Gagnep. In: Proceeding of the 2nd CMU graduate research

conference. 26 November 2010. Graduate school. Chiang Mai University; 2010. p. 4.

development of IPM in Asia and Africa, 7-9 December 2009; Bandar Lampung,

[24] Thepsukhon A, Choonluchanon S, Tajima S, Nomura M, Ruamrungsri S. Identification of endophytic bacteria associated with n2 fixation and indole acetic acid synthesis as growth promoters in *Curcuma alismatifolia* Gagnep. Journal of Plant Nutrition. 2013; 36(9): 1424-1438. DOI: 10.1080/01904167.2013.793712

[25] Ruamrungsri S, Ohyama T, Konno T, Ikarashi T. Deficiency of N, P, K, Ca, Mg or Fe mineral nutrients in *Narcissus* cv. 'Garden Giant'. Soil Science and Plant Nutrition. 1996; 42(4): 809-820. https:// doi.org/10.1080/00380768.1996.10416628

[26] Ruamrungsri S, Ohyama T, Ikarashi T. Nutrients, free amino acid and sugar contents in *Narcissus* roots affected by N, P, K deficiency during winter. Soil Science and Plant Nutrition.

[27] Ruamrungsri S, Ruamrungsri S, Ikarashi T, Ohyama T. Uptake, translocation and fractionation of nitrogen in *Narcissus* organs by using 15N. Acta Horticulturae. 1997; 430: 73-78. https://doi.org/10.1080/00380768

1996; 42(4): 765-771.

.1996.10416624

[23] Ruamrungsri S, Hamtisong N, Choonluchanon S. Selection of endophytic bacteria from *Curcuma alismatifolia* for N2 fixation and IAA synthesis. In: Proceedings of the 3rd International meeting for the

44(3): 145-153.

Indonesia.

Ohyama T. Assimilation and

j.1438-8677.2009.00229.x

translocation of nitrogen and carbon in *Curcuma alismatifolia* Gagnep. Plant Biol. 2009; 12: 414-423. DOI: 10.1111/

[16] Anuwong C, Ohyama T, Sueyoshi K, Ohtake N, Ruamrungsri S. Anatomical investigation of root and leaf of *Curcuma alismatifolia* Gagnep. Cv Chiang Mai Pink affecting N uptake. In: Proceedings of international graduate research conference Chiang Mai University. Chiang Mai, Thailand: 2014. p. 157-162.

[17] Marschner H. Mineral Nutrition of

[18] Anuwong C, Ohyama T, Sueyoshi K, Ohtake N, Sato T, Ruamrungsri S. Uptake and translocation of nitrogen in Patumma (*Curcuma alismatifolia*) from foliar spray or root application. Journal of Plant Nutrition. 2017; 40(8): 1204- 1212. DOI: 10.1080/01904167.2016.

Higher Plants. Academic Press. Amsterdam; 1986. 674 p.

[19] Khuankaew T, Tanabata S,

[20] Hongpakdee P, Ohtake N,

Sueyoshi K, Ohyama T, S Ruamrungsri. Effects of low night temperature and short-day length on some phytohormones and nutrient status in *Curcuma alismatifolia* Gagnep. Thai journal of Agricultural Science. 2010;

[21] Inkham C, Sueyoshi K, Ohtake N, Ohyama T, Ruamrungsri S. Effect of temperature and nitrogen sources on growth and nitrogen assimilation of *Curcuma alismatifolia* Gagnep. Thai

Yamamoto M, Ishikawa S, Tsutsumi K, Ohtake N, Sueyoshi K, Ohyama T, Sato T, Anuwong C, Ruamrungsri S. Temperature affects N and C assimilation and translocation in *Curcuma alismatifolia* Gagnep., The Journal of Horticultural Science and Biotechnology. 2014; 89(3): 287-292. DOI: 10.1080/14620316.2014.11513081

1264597

**130**

43(3): 163-173.

[28] Ruamrungsri S, Ruamrungsri S, Ikarashi T, Ohyama T. Ammonium and nitrate assimilation in Narcissus roots. J. Hortic. Sci. Biotech. 2000; 75: 223-227. https://doi.org/10.1080/14620316.200 0.11511227

[29] Hew CS, Yong JWH. The physiology of tropical orchids in relation to the industry. World Scientific: New Jersey; 1997. 370 p.

[30] Panjama K, Ohyama T, Ohtake N, Sato T, Potapohn N, Sueyoshi K, Ruamrungsri S. Identifying N sources that affect N uptake and assimilation in *Vanda* hybrid using 15N tracers. Horticulture, Environment, and Biotechnology. 2018; 59(6): 805-813. DOI: 10.1007/s13580-018-0071-6

[31] Ruamrungsri S, Khuankaew T, T Ohyama. Nitrogen sources and its uptake in *Dendrobium* orchid by 15N Tracer study. Acta Horticulturae. 2014; 1025: 207-212. DOI: 10.17660/ActaHortic.2014.1025.30

[32] Ruamrungsri S, Samanit P, Pornsawatchai T, Potapohn N, Fukai S. Effect of fertilizer application on nutritional concentrations and flower quality of Phalaenopsis hybrid. Acta Horticulturae. 2007; 755: 495-498. DOI: 10.17660/ActaHortic.2007.755.68

[33] Wang YT. Phalaenopsis mineral nutrition. Acta Horticulturae. 2010; 878: 321-334. DOI: 10.17660/ActaHortic. 2010.878.41

[34] Tunprasas W, Ruamrungsri S. Effect of nitrogen on cut rose quality in potted production. J of Agriculture. Chiang Mai university. 2016; 32(3): 339-345.

**133**

nutrition.

**Chapter 9**

**Abstract**

Nitrogen Storage in Crops: Case

Crop grains accumulate significant amounts of nitrogen in the form of storage proteins. Grain storage proteins are not only important in the aspects of germination but also, storage proteins are a valuable food source in human and animal nutrition. This chapter will give insight into genotype and growing conditions influencing the quantity and quality of storage proteins, primarily maize storage proteins the leading cereal by world production. Main storage proteins in cereals are prolamins, and in maize prolamins are called zeins located within the endosperm in protein agglomerations called protein bodies. Four main classes of zein proteins are: alpha, beta, gamma and delta zein. Each of four zein classes has a distinctive position and role within protein bodies. Prolamin proteins define nutritional value of maize grain not only via amino acid quality but also via starch availability. Starch, the most important energy component of maize grain, is located within starchprotein matrix. Within this matrix, starch granules are surrounded by protein bodies that limit starch availability. In this chapter, we will describe how zein proteins

*Marija Duvnjak, Kristina Kljak and Darko Grbeša*

influence characteristics of maize grain and nutritional value of maize.

maize nutritional value, animal nutrition

ization (0.05 Mg ha−1 year−1) [1].

**1. Introduction**

**Keywords:** maize grain, zein proteins, starch, amino acid quality, starch digestibility,

The protein content in cereal grain can vary greatly, from less than 6% to more than 20% in dry matter (DM), and this content depends on several factors such as the type of cereal, variety, agrotechnical conditions and others. These factors can be divided into two major groups, genotype and environment. Today, producers manipulate these factors to obtain grain of good quality and high protein content. For example, it has been found that the rate of maize yield gain is significantly higher after application of 220 kg N ha−1 (0.12 Mg ha−1 year−1) than without fertil-

In terms of their functions, there are three types of cereal grain proteins: structural proteins (as membrane proteins), metabolic proteins (as enzymes and enzyme inhibitors) and storage proteins, the largest fraction occurring primarily in starchy endosperm. Storage proteins account for 70–80% of the total protein content in grain and have a unique structure. The primary function of storage proteins is to supply grain embryo with nitrogen and amino acids during germination. However, these proteins are also a valuable food and feed source in human and animal

Study of Zeins in Maize

#### **Chapter 9**


**8. Conclusion**

carried out by Grimes et al. [89] revealed that the electrical response of as fabricated MWCNTs is significantly influenced by the presence of residual catalyst metal particles.

The electrical conductivity of CNT reinforced polymer composites makes them a very suita‐ ble candidate to be employed for electromagnetic interference (EMI) shielding. EMI is the process by which disruptive electromagnetic energy is transmitted from one electronic de‐ vice to another via radiation or conduction. As we all know that the electromagnetic waves produced from some electronic instrument have an adverse effect on the performance of the other equipments present nearby causing data loss, introduction of noise, degradation of picture quality etc. The common example is the appearance of noise in television signal when a telephone or mobile rings. Also recent reports of deterious effects of electromagnetic radiations on electro medical devices have caused concern among health care providers. The overlapping of signals transmitted in air traffic system with signals from other electronic equipments became cause of several accidents in past. Also mobile phones and passing taxi radios have been known to interfere with anti-skid braking system (ABS), airbags and other electronic equipments causing drivers to lose control. In today's scenario where rapid com‐ munication is required, there is an increase in electromagnetic radiations within the spec‐ trum in which the wireless, cordless and satellite system operates. So it a strong desire to shield electronics equipments from the undesired signals. Problems with EMI can be mini‐ mized or sometime eliminated by ensuring that all electronic equipments are operated with a good housing to keep away unwanted radio frequency from entering or leaving. The shielding effectiveness (SE) of the shielding material is its ability to attenuate the propaga‐

tion of electromagnetic waves through it and measured in decibels (dB) given by

of 10 dB means 90% of signal is blocked and 20 dB means 99% of signal is blocked.

and *P*0 are, respectively, the transmitted and incident electromagnetic power. A SE

One of the important criterion for a material to be used for EMI shielding material is that it should be electrically conducting. Because of their high electrical conductivity metals have been used for past several years as EMI shielding materials. But the shortcomings of metals like heavy weight, physical rigidity and corrosion restricts their use. The most notable sub‐ stance that could overcome these shortcomings is the CNT-polymer composites. As dis‐ cussed in previous sections these are electrically conductive, having low density, corrosion resistant and can be molded in any form. Due to easy processing and good flexibility, CNT– polymer composites have been employed for application as promising EMI shielding mate‐ rials. The SE of the CNT-polymer composites depends on various factors like,type of CNTs (either SWCNT or MWCNT), aspect ratio of CNTs, quality of CNTs, thickness and electrical conductivity of the shielding material. Several studies have been reported on EMI shielding properties of randomly oriented CNT based polymer composites. Mathur and co-workers [18] have prepared MWNT-PMMA and MWNT-PS composites and observed 18dB and 17dB SE respectively with 10-wt % MWCNT loading. Singh et al. [90] reported a SE of 51 dB

*SE*(dB) = −10 log(*Pt* / *P*0),

where *P*t

**7.3. EMI shielding proerties of MWCNTs polymer nanocomposites**

214 Syntheses and Applications of Carbon Nanotubes and Their Composites

Synthesis of high quality and reproducible CNTs is still remain a very importnat issue. Chemical vapor deposition has been found an efficient process for the synthseis of bulk quantity of CNTs. The CNT-polymer composites have been developed with improved me‐ chanical properties but for actual structural applications, these have to compete with the ex‐ isting carbon fibre based composites. Dispersion of high loading of CNTs and their alignment in any polymer matrix without sacrificing their mechanical properties is still a challenge for using CNTs in high performance composites for specific applications such as as automobile, defence, aerospace, sports etc. CNT- carbon fibres-polymer multiscale com‐ posites could be an alternative route for further improvement in the mechanical properties of the composites over commercially available CF-polymer composites. Till then electrical properties of CNT polymer composites provides exciting possibility as antistatic and electro‐ magnetic interference shielding material.


The balance and postural stability of stroke survivors have been quantified using kinematic and kinetic parameters, such as joint angles, joint moment, end-point distance, the distance between the center of mass and the center of pressure, and the ground reaction force (6–8). The balance was compromised in stroke survivors, and they might take a strategy involving greater pelvic posterior tilt and greater joint angles to ensure enough toeobstacle clearance compared with healthy controls during obstacle crossing. However, little has been investigated about the neuromuscular changes during obstacle crossing. Electromyography (EMG) signals recorded from the surface of the muscles show the activity of motor neurons, which can reflect the relative level of muscle activation and provide valuable information about muscle function (9). Hahn et al. compared the EMG signals of the lower limb muscles between the elderly and the young (10). They found that elderly adults were able to negotiate different heights during walking and that the higher muscular demands could lead to a high risk of falls. The level of activity was reduced in the hemiparetic muscles in stroke survivors compared with the normal subjects, while the muscle's activity of the non-paretic side was increased compared with the normal subjects, which helped maintain standing balance in response to sideways pushes (11). However, the neuromuscular mechanism of maintaining balance when crossing obstacles of different heights remains to be investigated in stroke survivors. Stepping over obstacles of different heights requires varying the activation levels of the agonist and antagonist muscles. Muscle cocontraction is the simultaneous activity of agonist and antagonist muscles (12, 13). Kitatani et al. demonstrated increased muscle coactivation in the trailing limb of stroke survivors with increasing obstacle heights, which could increase postural stability and decrease the rate of trips (14). However, they did not compare the coactivation patterns between healthy subjects and stroke survivors during obstacle crossing to understand more about the coordination mechanism following stroke.

Electromyography signals can also be analyzed in the frequency domain based on the power spectrum to reflect the neuromuscular function. The mean power frequency (MPF) and median frequency (MF) mainly reflect changes in the conduction velocity of the active motor units which could be damaged after stroke (15). It has been speculated that alternations of the EMG spectrum are related to loss of muscle fibers, changes in the composition of motor unit type (peripheral), synchronization of multiple motor units, and disorder control of motor unit (central factors). Many studies have investigated the relationship between muscle activation level and MPF and MF values for both healthy people and stroke survivors. Decreased MPF values are usually found in the muscles of the paretic side compared with the non-paretic side (16, 17). The amplitude of the EMG signals increases with the level of muscle force, but studies on the relationship between the EMG spectrum and contraction force remain uncertain. Hu et al. found slightly decreased MF values in the paretic biceps brachii with increased muscle contraction in stroke survivors (18), while Kaplanis et al. reported increased MF in the EMG of the biceps brachii with increased isometric torque in healthy subjects (19). EMG spectral analysis could help better understand the cause of neuromuscular changes in the stroke survivors during obstacle crossing, which has not been investigated in previous studies.

This study investigates neuromuscular changes in stroke survivors to maintain balance when crossing obstacles of different heights in comparison with healthy controls. We examined the relative muscle activation levels, the cocontraction of the agonist and antagonist muscles of the knee joint and ankle joint, and the power spectrum of the muscles, also their relationship with the clinical scales. The results may provide knowledge of the mechanism of motor control during obstacle crossing and information for designing fall prevention programs for rehabilitation following stroke.

## MATERIALS AND METHODS

#### Participants

Eight stroke survivors and eight healthy subjects matched by age, height, and gender were recruited in this study (**Table 1**). The inclusion criteria for the stroke survivors included (i) the occurrence

TABLE 1 | Basic characteristics of study subjects.

*a Significant effect using an independent t-test.*

*FMA, Fugl-Meyer assessment scale of the motor function in paretic low-extremity (total score: 34).*

of a first stroke with unilateral hemiparesis lesions confirmed by magnetic resonance imaging or computed tomography; (ii) an interval of at least 3 months post-stroke; (iii) the ability to step across an obstacle height of 30% leg length; and (iv) the ability to sign an informed consent form. This study was approved by the Ethics Committee of the First Affiliated Hospital of Sun Yatsen University. All procedures were conducted according to the Declaration of Helsinki and all subjects provided written consent before the experiments. The motor function of stroke survivors was evaluated by an experienced physiotherapist based on the Berg Balance Scale (BBS) and Fugl-Meyer assessment (FMA) for lower extremities.

#### Apparatus

Circular silver–silver chloride (Ag–AgCl) electrodes with a diameter of 5 mm and inter-electrode distance of 20 mm were bilaterally attached to the bellies of the rectus femoris (RF), biceps femoris (BF), tibialis anterior (TA), and medial gastrocnemius (MG) of the lower limbs of the subjects. The muscle groups showed obvious changes when stepping over obstacles (20). Eight pre-amplified wireless transmission modules (DTS Noraxon, Scottsdale, AZ, USA) at a gain of 4,000 were linked with the electrodes to record EMG signals at a sample frequency of 1 kHz.

A total of 35 spherical 15-mm infrared-reflective markers were fastened to the subject's whole body according to the Vicon Plug-In Gait marker placement. A 6-camera 3D motion analysis system (Vicon Motion Systems, Oxford, UK) recorded the marker positions at a sample frequency of 100 Hz. Two force plates (464 mm × 508 mm × 83 mm, AMTI, Watertown, MA, USA) at a sample frequency of 1 kHz were placed in the middle of the path with the obstacle between them. All the EMG, kinematic, and kinetic signals were recorded simultaneously and processed by the Vicon Nexus operating system.

#### Procedure

Anthropometric characteristics were measured before the gait analysis (height, leg length, and bodyweight). Leg length was measured with scaled tape from the anterior superior iliac spine to the lateral malleolus and used to calculate the obstacle height of each individual. The electrodes and spherical markers were then attached to the corresponding locations on the subject. Before the electrode placement, the area around the muscles were shaved and cleaned with alcohol, and surgical tape was used as appropriate around the electrode and amplifier to obtain better EMG signals.

When the preparation was finished and the subjects had enough rest, the subjects were instructed to walk at a self-selected speed with bare feet along an 8-m walkway with a height-adjustable obstacle placed midway. The leading limb was defined as the first limb to cross the obstacle. The stroke subjects were instructed to use their affected leg as the leading limb of the obstacle crossing, and the healthy controls were instructed to use their dominant leg. The obstacle was set to three height conditions (10, 20, and 30% leg length). The three height conditions were performed in random order, and three trials of each height condition were recorded. Trials in which the subjects touched the obstacle were ignored and excluded from analysis. Prior to the trials, subjects visually and manually inspected the obstacle, and then practiced two to three trials according to the therapist's instructions. Subjects were reminded to perform the task within their limits of safety and stop if they felt at risk. A therapist accompanied the subject and walked to the side to provide protection and assistance if required.

After all the trials were finished, the subject was asked to rest for a few minutes and then instructed to lay supine with the tested limb placed at 90° hip and knee flexion, and the other limb resting in neutral to perform maximum voluntary contraction (MVC) tests (21). Another experienced therapist held a handheld dynamometer (MicroFET3, Hoggan Inc., UT, USA; with the precision of 0.4 N and range from 13 to 1,330 N) stably as a resistance at the corresponding position of the measured joint (22), and the subject used tested muscle group to push maximally against the hand-held dynamometer for about 5 s. To measure the MVC of the TA and MG, the hand-held dynamometer was placed proximally to metatarsophalangeal joints on dorsal and plantar surface of foot. To measure the MVC of the RF and BF, the hand-held dynamometer was placed proximally to ankle on anterior and posterior surface of leg (23). The subject performed 2–3 submaximum voluntary contractions before the MVC test began to become familiar with the test procedure. MVC test included knee flexion and extension, dorsiflexion, and plantar flexion, and the MVC was recorded three times for each muscle. During the MVC procedure, subjects were verbally encouraged to ensure maximal recruitment.

## Data Processing

For all MVC and gait trials, raw EMG signals were collected at 1 kHz, band-pass filtered through a fourth-order Butterworth filter with a bandwidth of 10–350 Hz, full-wave rectified, and low-pass filtered through a second-order Butterworth filter with a cut off frequency of 6 Hz. The root mean square was calculated during a particular phase of the gait. The filtered signals from the gait trials were then normalized to the MVC for each muscle to determine the relative activation levels. The calculation of the cocontraction index (CI) required two more steps with a linear envelope: (1) subtraction of the average resting EMG activity and (2) normalization to the maximum value of EMG activation in each muscle during the MVC tests (24). The CI value is given by

$$\mathrm{CI} = \frac{1}{T} \int\_{\mathbb{T}} A\_{\vec{\eta}} \left( t \right) dt$$

where *Aij*(*t*) is the overlapping activity of EMG linear envelopes after subtraction and normalization for the agonist and antagonist muscles *i* and *j*, *T* is the length of the signal period. The CI value varies from 0 to 1. Zero means there is no overlapping of the two EMG envelopes, and 1 means the two muscles are fully activated to 100% MVC during the trial.

The MPF was calculated using the band-pass filtered signals (through a fourth-order Butterworth filter with a bandwidth of 10–350 Hz) for each time window (the stance, swing, or entire gait cycle). The MPF value is given by

$$\text{MPF} = \frac{\overset{\circ}{\int} f P(f) df}{\overset{\circ}{\int} P(f) df}$$

where *P*(*f*) indicates the power intensity curve and *f* indicates the frequency. The kinematic data were filtered by a 20-Hz low-pass Butterworth filter. We considered the toe-off time to be when the toe marker was 2 mm off the ground and the heel-strike time as when the force platform received enough signals to make the measurement reliable (25). The gait cycle was then divided into a swing phase and stance phase for a single lower limb.

#### Statistical Analysis

Descriptive statistics (mean values and SD) were calculated for all dependent variables. The normalized EMG activation, CI, and MPF values were subjected to a repeated-measures two-way (group: stroke and control × obstacle height: 10, 20, and 30% of leg length) analysis of variance (ANOVA). The ANOVA results were adjusted using a Bonferroni *post hoc* test. If there was an interaction between the two effects, then one-way ANOVA was separately performed for the group effect and height effect. Pearson product–moment correlations were used to examine the relationships between the calculated variables and clinical scales. The level of significance was set at an alpha level of 0.05. All statistical analyses were done using SPSS 19 statistical software.

#### RESULTS

All subjects were able to complete the tasks with three different obstacle heights and the MVC tasks. No incidents of falling were observed, and we discarded the trials in which the subjects touched the obstacle or received help from the therapist. Subjects did not indicate discomfort during the tasks, nor did they show any feelings of fatigue.

The rectified and normalized EMG signals showed that the muscles were activated at a corresponding phase during the gait in a typical trial of a stroke subject and control subject (**Figure 1**). The muscle activation level and activation duration were greater among the stroke survivors than the healthy controls in the four muscles of the leading limb respectively. Also, there were some abnormal cocontractions among stroke survivors. For example, the TA and MG (a pair of agonist and antagonist muscles) had greater coactivation level in the stroke survivor (CI = 12.15%) compared to the healthy control (CI = 4.31%) during the swing phase of the obstacle crossing, as indicated by a circle in **Figure 1**.

The stroke survivors showed greater relative activation levels in the leading and trailing limbs compared to healthy controls (**Table 2**). For all height conditions during the swing phase, the TA activation of the stroke survivors reached an average of 41% of their maximum capacity, in contrast to 29% in the healthy controls. Interactions were found between the groups and heights in the TA muscle of the leading limb during both swing and stance phases. The activation of the TA of the leading limb significantly increased with the obstacle height during both swing and stance phases (one-way ANOVA with *post hoc* tests, *p* < 0.05). The TA activation was significantly greater in the stroke survivors compared to the healthy controls at the 20 and 30% heights during the swing phase and at 30% height during the stance phase (one-way ANOVA, *p* < 0.05). For the trailing limb, the activation of the BF and TA also significantly increased with the obstacle height during the swing phase (*p* < 0.05).

During the stance phase in the leading limb, the average CI for all height conditions of the RF and BF of the stroke survivors was 17.95 ± 7.90%, which was significantly greater than the average of 13.81 ± 4.89% (*p* < 0.05) for the healthy controls. Also, the average CI of the TA and MG was also significantly higher for the stroke survivors (10.53 ± 5.54%) than the healthy controls (7.60 ± 2.98%) during the swing phase (*p* < 0.05). Similarly, in the trailing limb, the average CI of the RF and BF (15.51 ± 5.18%) and the TA and MG (14.33 ± 8.52%) were significantly larger among stroke survivors than the healthy controls (RF and BF: 7.48 ± 2.39%, TA and MG: 7.85 ± 2.54%, *p* < 0.05), but only during the stance phase. The average CI showed no significant difference between stroke survivors and healthy controls in other conditions (*p* > 0.05).

**Figure 2** shows the details of the CI at each obstacle height for both the leading and trailing limbs during the swing and stance phases. For the CI of the two muscle pairs of the leading limb, a between-group difference was found at the 30% height in the RF and BF and the 20 and 30% heights for the TA and MG (*p* < 0.05). There was a significant difference between the 10 and 30% and the 20 and 30% heights for the RF and BF during the stance phase, as well as between the 10 and 30% and the 20 and 30% heights for the TA and MG (*p* < 0.05). For the trailing limb, between-group differences in CI were found during the stance phase except for the TA and MG at the 10% height (*p* < 0.05). There was also a significant difference between the 10 and 30% heights and the 20 and 30% heights for the TA and MG during the swing phase (*p* < 0.05, **Figure 2**).

The stroke survivors had significantly smaller global MPF values of the leading limb during the entire crossing gait cycle (RF: 130.18 ± 13.33 Hz, BF: 134.67 ± 18.69 Hz, TA: 145.35 ± 12.52 Hz, MG: 130.18 ± 13.51 Hz) than the healthy controls (RF: 139.26 ± 10.21 Hz, BF: 148.40 ± 9.57 Hz, TA: 151.89 ± 6.96 Hz, MG: 140.18 ± 17.49 Hz), respectively (*p* < 0.05). However, no significant difference was found in the MPF values of the trailing limb during the entire crossing gait cycle between groups (*p* > 0.05). **Figure 3** shows a more detailed comparison of MPF

vertical lines indicate the time of toe-off when crossing the obstacle. Gait cycle 0–1 indicates the cycle before the obstacle, gait cycle 1–2 indicates the crossing cycle, and gait cycle 2–3 indicates the cycle after the obstacle. The circle labeled zone is used to demonstrate the phenomenon of cocontraction.

values in the leading limb at each obstacle height during different phases. The MPF of the four muscles of the leading limb was significantly smaller in the stroke survivors than the healthy controls (*p* < 0.05). There was also a decreasing trend (no significant difference) for the MPF value of the muscles as the obstacle height increased in most conditions. However, the MPF value of the TA in the leading limb showed an increasing trend (no significant difference) with increasing obstacle height.

We also examined the correlation between the calculated variables including activation levels, CI, and MPF values and the clinical scales (FMA and BBS). We only found a significant positive correlation between the activation of BF and FMA (*r* = 0.802, *p* = 0.017) and a significant negative correlation between the activation of TA and FMA (*r* = −0.817, *p* = 0.013) during the swing phase at the 10% obstacle height (**Figure 4**). No significant difference was found in other situations or between other variables and clinical scales.

## DISCUSSION

In this study, we recorded the EMG signals of the four main lower limb muscles of stroke survivors and healthy controls

TABLE 2 | Normalized electromyography activation percentages during swing and stance phases for rectus femoris (RF), biceps femoris (BF), tibialis anterior (TA), medial gastrocnemius (MG) of both leading and trailing limbs: mean (SD).

*a Significant group effect.*

*bSignificant height effect.*

*c Significant interaction effect.*

*dSignificant group effect by post hoc test.*

Δ*,*□*,*○*Significant height effect by post hoc test. "*Δ*" means significant height effect between 10 and 20% leg length height. "*□*" means significant height effect between 10 and 30% leg length height. "*○*" means significant height effect between 20 and 30% leg length height.*

during obstacle crossing. Our study demonstrated the activation levels of the muscles and the coactivation of agonist and antagonist muscles were greater, and the MPF values of the muscles were lower in the stroke survivors than the healthy controls, which indicated abnormal patterns of the gait and obstacle crossing following stroke. In addition, the significant correlations between the muscle activation of BF, TA, and FMA provided a reliable method to analyze the muscles of stroke survivors.

#### Muscle Activation Level

When crossing the obstacles, the two groups encountered the same mechanical challenge (obstacle heights equal to the same percentage of leg length), but the stroke survivors showed greater overall muscle activation levels than the healthy controls in both the leading limb and the trailing limb (**Table 2**). Postural stability and the ability to maintain balance were impaired after stroke. Kirker et al. demonstrated that while a normal pattern of hemiparetic muscle activation was found in stepping, these muscles remained badly impaired in response to a perturbation and compensated by increased activity of the non-paretic muscles (11). Obstacle crossing challenges the stroke survivors to the limits of their capacity, and they are required to activate a greater level of their neuromuscular capacity to walk and safely step across obstacles. This may cause more serious postural instability (6). Similar results were also found in children with cerebral palsy. The RF and MG activation have been reported to increase in the swing phase of gait, which is considered an abnormal activation pattern that results from muscle weakness caused by cerebral palsy (26). According to Hahn et al., the relatively higher activation level might lead to muscular fatigue and place stroke survivors at higher risk of falls (10).

rectus femoris (RF) and biceps femoris (BF) of leading limb during swing phase. (B) The CI of RF and BF of leading limb during stance phase. (C) The CI of tibialis anterior (TA) and medial gastrocnemius (MG) of leading limb during swing phase. (D) The CI of TA and MG of leading limb during stance phase. (E) The CI of RF and BF of trailing limb during stance phase. (F) The CI of RF and BF of trailing limb during swing phase. (G) The CI of TA and MG of trailing limb during stance phase. (H) The CI of TA and MG of trailing limb during swing phase. The asterisk (\*) indicates significant effect between groups. The bar (-) indicates significant effect between heights.

We found that the activation level of TA increased with increasing obstacle height in the swing phase of both leading and trailing limbs in stroke survivors. The TA muscle is the primary ankle dorsiflexor and is activated from the initial swing to let the ankle joint turn into a neutral position. However, Antonopoulos et al. found no significant height effect of the activation of the TA muscle for young adults during obstacle crossing (27). The increased activation level of TA in stroke may related to spasticity. Because of the excessive coactivation of the antagonist (MG), the increased activation level of TA was necessary to ensure a safe clearance during obstacle crossing.

#### Muscle Cocontraction

Although cocontraction is inefficient for joint movement, it might be important for providing joint stability, especially in tasks like obstacle crossing (14). When the leading limb steps over an obstacle, the cocontraction of the TA and MG is greater in the stroke survivors compared with the healthy controls and also increased with increasing obstacle height (**Figure 2C**). The dorsiflexor (TA) strength is weakened after stroke, and the cocontraction of the antagonist (MG) might reduce the dorsiflexion range but increase the stability during the swing phase to ensure safe crossing (28). Also, the cocontraction

of thigh muscles (BF and RF) is greater in the post-obstacle stance phase of the leading limb of the stroke survivors compared with the healthy controls, which is helpful to maintain balance by controlling the knee position during loading (29) (**Figure 2B**).

The greater cocontraction of the two pairs of lower limb muscles with increasing height also indicated muscle weakness on the paretic side of stroke survivors, who needed greater cocontraction to maintain balance when crossing obstacles of higher heights. Muscle cocontraction is also related to postural stability

activation level of tibialis anterior (TA).

and dynamic strength in osteoarthritis (30), cerebral palsy (31), and Parkinson's disease (32). The increased muscle cocontraction is a metabolically costly process (33), but it may help preserve some mobility in those with weakness (34).

When the trailing limb steps over the obstacle, the cocontraction of the TA and MG also showed a height effect, but there was no significant difference between groups during the swing phase (**Figure 2H**). This indicates that the cocontraction might be an adaptation strategy for both stroke survivors and healthy controls to increase postural stability. Both of the two pairs of lower limb muscles showed greater cocontraction in stroke survivors during the trailing limb stance phase (**Figures 2E,G**), which could be attributed to the need for greater stability and motor adaptation to the weakness of the affected leading limb to support body weight (35, 36). With motor recovery, the muscle strength could be increased and enable a more efficient strategy with decreased muscle cocontraction. Hu et al. investigated the motor function recovery process in cases of chronic stroke and found that CI values decreased during the recovery process as motor function improved (24). Assessing muscle cocontraction helps us to understand the coordination mechanism in stroke survivors and the adaptation strategy they use to ensure safe crossing.

## Analysis of Power Spectrum

The overall power spectrum analysis of all subjects indicated that stroke survivors had reduced MPF of the surface EMG in the leading limb compared with healthy controls (**Figure 3**). The reduction in MPF or MF on the paretic side has been report previously (16, 17). The decrease in MPF values of the paretic muscles could be due to the loss of muscle fibers and impairments in the motor unit following stroke. The firing rate has been demonstrated to be lower on the paretic side of stroke survivors compared both with the non-paretic side of stroke survivors and with the healthy controls during contraction (37, 38), which might also cause the reduced MPF values.

Alternations in the EMG spectrum have also been reported in children with cerebral palsy. Wakeling et al. and Gestel et al. found increased MPF value in such children compared with asymptomatic controls during gait and related it to the muscle dysfunction (26, 39). The assessment of the EMG spectrum could be used as an evaluation tool for functional muscle strength. No significant difference of MPF was found in the trailing limb between stroke survivors and healthy controls. This might be due to our instruction to the stroke survivors to use their affected side to cross the obstacle first and to use the unaffected limb as the trailing limb. This might have taken less effort to cross the obstacle and resulted in the lack of a significant between-group difference.

Obstacle crossing is a complex task, and the different heights place different demands on the subjects. Both groups showed a decreasing trend in MPF value with increasing obstacle height in the leading limb except for the MPF of the TA (**Figure 3**). Gabriel and Kamen also reported a significant decrease in the spectra with the force in the biceps brachii (40). With increased muscle activation level, there might be an increased degree of motor unit synchronization for the stroke survivors with the increasing obstacle height, which was likely to lead to reduction in the MPF values and fatigue (41, 42).

## Correlation

One interesting finding in our study is that there were significant correlations between calculated muscle activation levels of BF and TA in the leading limb and FMA scores measured by the therapists during swing phase at 10% leg length height (**Figure 4**) in stroke survivors. No significant correlation was found between muscle activation levels and FMA scores during 20 or 30% leg length height obstacle crossing in stroke survivors. Performance of stroke survivors was more disturbed during challenging task such as for obstacle crossing of higher height (43), and they couldn't take good control of themselves which led to abnormal patterns and large variations within group when step across the obstacle. The activation level of the BF increased while that of the TA decreased with increasing obstacle height. Proximal control is more efficient than distal control in the lower limbs for stroke survivors to ensure safe obstacle crossing according to kinematic analysis (7). Our research showed through EMG analysis that stroke survivors with high FMA scores had greater BF activation, which controlled the hip joint and knee joint to elevate the toe. At the same time, the TA (which is related to ankle dorsiflexion) was abnormally activated to a larger degree in the face of fall risk among stroke survivors with lower FMA scores. Similar to Li et al., we found no significant correlation between the MPF values and clinical scales or between CI and clinical scales (16). Nevertheless, the significant correlation between the muscle activation levels and FMA scores means that it is reliable to use EMG signals to analyze the muscles of stroke survivors.

#### Limitations

This study has several limitations. The recruited stroke survivors in this study had good function, so the results might not be useful for moderate or severe stroke survivors. We instructed the stroke survivors to use their hemiparetic side to take the first step over the obstacle and to use the unaffected side as the supporting limb, which is safer for them. We neglected situations using the other limb as the leading limb, which might lead to a minor difference in the results. Moreover, significant differences between stroke survivors and healthy controls should also be interpreted with caution considering the relatively small sample size. We plan to recruit more stroke survivors of different functional levels and to instruct them to use both limbs as the leading limb in future work to obtain a more comprehensive understanding of the neuromuscular changes.

#### Conclusion

In this study, stroke survivors were recruited to step across obstacles of three different heights and compared with healthy controls to investigate motor control mechanisms that could not be reflected during level walking. Although the stroke survivors could safely step across the obstacles, they demonstrated abnormal motor control patterns, such as greater overall muscle activation level and larger cocontraction of the agonist and antagonist muscles. These might result in muscle fatigue, which would lead to a high risk of tripping and higher energy cost. The reduction in the MPF values of the paretic side of stroke survivors could be related to impairments of the motor unit or other complex neuromuscular alterations. The decreasing trend

## REFERENCES

of the MPF values when crossing higher heights might due to greater motor unit synchronization, which could also lead to fatigue. The significant correlations between muscle activation levels and clinical scales provided a reliable method of analyzing the muscle functions of stroke survivors. These findings could help therapists to understand the neuromuscular changes following stroke and work out specific methods for rehabilitation of the lower limb muscles.

#### AUTHOR CONTRIBUTIONS

CM analyzed the data, interpreted the results, and wrote the draft of the manuscript. NC conducted the experiment and collected the data. YM and DH participated in the experiment and interpreted the results. RS participated in data analysis and interpretation and revised the manuscript. LL designed the study and performed all stages of the study including data collection, analysis, interpretation, and substantial revision of the manuscript. All the authors approved the final version of the manuscript.

## FUNDING

This work was supported in part by the National Natural Science Foundation of China under Grant 31100669 and Guangdong Science and Technology Department (No. 2015B020214003, 2015A050502022, 2014B090901056, 2013b090500099) and partly supported by Natural Science Foundation of Guangdong Province, China (No. 2015A030313139) and Guangzhou Research Collaborative Innovation Projects (No. 2014Y2-00507).

*J Electromyogr Kinesiol* (2009) 19(1):157–71. doi:10.1016/j.jelekin.2007. 03.010

**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 Ma, Chen, Mao, Huang, Song and Li. 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.*

# **Wavelet Packet Feature Assessment for High-Density Myoelectric Pattern Recognition and Channel Selection toward Stroke Rehabilitation**

*Dongqing Wang1 , Xu Zhang1 \*, Xiaoping Gao2 , Xiang Chen1 and Ping Zhou3,4,5*

*1Department of Electronic Science and Technology, University of Science and Technology of China, Hefei, China, 2Department of Rehabilitation Medicine, First Affiliated Hospital of Anhui Medical University, Hefei, China, 3Department of Physical Medicine and Rehabilitation, University of Texas Health Science Center at Houston, Houston, TX, USA, 4 TIRR Memorial Hermann Research Center, Houston, TX, USA, 5Guangdong Work Injury Rehabilitation Center, Guangzhou, China*

This study presents wavelet packet feature assessment of neural control information in paretic upper limb muscles of stroke survivors for myoelectric pattern recognition, taking advantage of high-resolution time–frequency representations of surface electromyogram (EMG) signals. On this basis, a novel channel selection method was developed by combining the Fisher's class separability index and the sequential feedforward selection analyses, in order to determine a small number of appropriate EMG channels from original high-density EMG electrode array. The advantages of the wavelet packet features and the channel selection analyses were further illustrated by comparing with previous conventional approaches, in terms of classification performance when identifying 20 functional arm/hand movements implemented by 12 stroke survivors. This study offers a practical approach including paretic EMG feature extraction and channel selection that enables active myoelectric control of multiple degrees of freedom with paretic muscles. All these efforts will facilitate upper limb dexterity restoration and improved stroke rehabilitation.

**Keywords: myoelectric control, pattern recognition, wavelet packet transform, channel selection, stroke rehabilitation**

## **INTRODUCTION**

Restoration of upper limb function is an important but challenging task in stroke rehabilitation due to arm/hand dexterity (which is critical for daily activities). A number of upper limb robotic devices have been designed to assist rehabilitation training for promoting upper limb motor recovery (1, 2) among which some recently emerging ones involve different human–machine interfaces enabling active response to user's intention. Compared with passive training, such an active control approach has proven to be more effective for motor function improvement (3, 4).

Electromyogram (EMG) is one of the most commonly used control signals for artificial limbs, rehabilitation robots, and other assistive devices (5–7). Most development in myoelectric control is primarily based on a simple control strategy that the EMG of a single muscle is mapped to a single degree of freedom (DOF). Considering the complexity of upper limb functional movements performed by multiple muscles, it is unfeasible to control multiple DOFs through such a straightforward mapping (8). Because of this, myoelectric pattern recognition has been developed for controlling of

#### *Edited by:*

*Xiaogang Hu, University of North Carolina at Chapel Hill, USA*

#### *Reviewed by:*

*Muhib Khan, Michigan State University, USA Francesco Negro, University of Göttingen, Germany*

*\*Correspondence:*

*Xu Zhang<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Stroke, a section of the journal Frontiers in Neurology*

*Received: 08 August 2016 Accepted: 25 October 2016 Published: 21 November 2016*

#### *Citation:*

*Wang D, Zhang X, Gao X, Chen X and Zhou P (2016) Wavelet Packet Feature Assessment for High-Density Myoelectric Pattern Recognition and Channel Selection toward Stroke Rehabilitation. Front. Neurol. 7:197. doi: 10.3389/fneur.2016.00197*

**194**

multiple DOFs (8–10). So far, the myoelectric pattern recognition control strategy has been primarily focused on improving dexterity of prosthesis control for amputee users, whereas its application for neurological injury patients has not been fully explored (5). Only very recently, myoelectric pattern recognition was first reported to detect movement intention of affected limb after stroke (8). A more comprehensive assessment of neural control information from paretic muscles of stroke subjects was further performed using high-density surface EMG recording and pattern recognition techniques (4).

While high-density surface EMG pattern recognition has revealed substantial neural control information that can be extracted from neurologically impaired muscles, there are a number of issues to be considered for developing a myoelectric control system. These include assessment of different EMG features, selection of a practical number of appropriate EMG channels (myoelectric control sites), and user-specific design according to individual need and performance. A variety of features describing surface EMG signals in different (time, frequency, time–frequency, etc.) domains have been used for myoelectric pattern recognition analysis, but primarily aimed at prosthetic control (9–12). So far for patients with neurological injuries, the myoelectric pattern recognition analysis has been limited to using conventional timedomain (TD) feature set [four time domain statistics proposed by Hudgins et al. (9) and auto-regressive (AR) + root mean square (RMS) feature set (a combination of AR coefficients and RMS amplitude) (5)]. Assessment of EMG features for neurological injury patients might be promising to improve myoelectric pattern recognition performance, particularly given the neurologic injury induced muscle impairments (such as weakness, spasticity, and abnormal coactivation) (13).

Time–frequency analysis has been developed as a useful tool for processing non-stationary biosignals (such as EMG). Time–frequency representations of surface EMG such as using wavelet packet transform (WPT) can also be applied in myoelectric pattern recognition, as demonstrated in amputees or ablebodied subjects (10, 14, 15). In the current study, the utility of applying WPT to stroke subjects was examined. The WPT is able to generate a redundant set of subspaces arranged in a binary tree structure with any designed depth/resolution, where the input signal can be accordingly decomposed. Performing wavelet packet analysis of surface EMG recordings from paretic muscles has several advantages. For example, its high resolution in both time and frequency domains makes it feasible to produce a sufficient number of features, from which those highly associated with different movement intentions of the affected limb (i.e., discriminable features) can be easily selected *via* a best basis selection approach to maximize the pattern separability (15, 16). Moreover, such a feature selection approach can be expanded for selecting surface EMG channels (myoelectric control sites) from the high-density surface EMG recordings (by adopting the same discriminant measure). The advantages of the WPT analysis for myoelectric pattern recognition and channel selection were demonstrated for stroke patients. These findings provide useful information for developing a pattern recognition-based myoelectric control system for stroke rehabilitation.

## **METHODS**

## **Dataset Description**

The dataset used in this study was selected from a database previously reported in Zhang and Zhou (4), which was approved by the Institute Review Board of Northwestern University (Chicago, IL, USA). This database included high-density surface EMG recordings from 12 chronic stroke subjects with hemiparesis during their performance of different functional movements involving the affected upper limb, notably the affected hand. The detailed demographic information and clinical assessment for the stroke subjects can be found in Ref. (4). All subjects gave their informed consent before the experiment. **Table 1** displays demographics and clinical information of all stroke subjects in detail.

During the experiment, each subject was instructed to perform 20 functional movements using the affected upper limb, namely, wrist flexion/extension, wrist supination/pronation, elbow flexion/extension, hand open/close, thumb flexion/extension, index finger flexion/extension, finger 3–5 flexion/extension, fine pinch, lateral pinch, tip pinch, gun posture, and ulnar wrist down/up. A video demonstration of each movement was used as a guide for subjects to follow and perform the movement. The experiment protocol comprised of 20 trials, each trial consisting of 5 repetitions of the same movement. For each repetition, the subject was asked to hold the muscle contraction for roughly 3 s and then relaxed for a rest period of 5–20 s.

The high-density surface EMG signals in the original database were recorded *via* 89 monopolar surface electrodes placed on the affected upper arm, forearm, and hand muscles. A Refa EMG recording system (TMS International BV, Netherlands) with a band-pass filter between 20 and 500 Hz was used for multichannel EMG recording at a sampling rate of 2 kHz per channel. Due to improved myoelectric classification performance and more clinical relevance compared with monopolar configuration, 46-channel bipolar surface EMG data were produced from the original 89-channel EMG recordings. The detailed information about the electrode formation and single spatial differential filter is shown in **Figure 1**. Besides, 10 bipolar channels, namely, the channel 9, 13, 17, 19–21, 23, and 41–43 were selected from

*Duration, years post stroke; paretic, the side of hemiparesis; FMUE, the Fugl-Meyer assessment scale of the paretic upper-extremity (total score: 66); C–M hand, the hand impairment part of the Chedoke–McMaster stroke assessment scale (from 1 to 7).*

the 46 channels to form a channel set. The selection of such a channel set was in accordance with electrode sites frequently used in many previously reported myoelectric control systems (8). These channels were regarded to target at primary muscles with high relevance to functional movements of the upper limb, as marked in a black/darker color in **Figure 1**. In this study, such an empirically defined channel set was compared with all 46 highdensity channels or a number of optimally selected channels in terms of myoelectric control performance.

For the recorded signals, the onset and offset of a voluntary EMG activity segment corresponding to each repetition of muscle contraction were determined first as described in Ref. (4). For each repetition of muscle contraction, the EMG activity segment in a form of multiple channels was further segmented into a series of overlapping analysis windows with a window length of 256 ms

and an overlapping rate of 75% for two consecutive windows. Consequently, the following feature extraction and classification procedures were performed on these analysis windows.

## **Feature Extraction Using WPT**

The WPT was a generalized version of classical wavelet decomposition method that offers a multi-resolution and time–frequency analysis of non-stationary, such as biomedical signals (17, 18). Define the original signal space as Ω0,0. The WPT is able to split the signal into an approximation (in subspace Ω1,0) and a detail (in subspace Ω1,1). Each approximation or detail obtained from the top-level, supposed in the subspace Ω*j*,*k*, can be further split into a new approximation and a new detail, located in two orthogonal subspaces Ω*j*+1,2*k* and Ω*j*+1,2*k*+1, respectively. This process can be iteratively performed to a targeted depth *J*. Here, *j* is a scale index ranging from 0 to *J*, and *k* represents subband index within the scale, ranging from 0 to 2*j −* 1. Consequently, the WPT generates a binary tree structure of subspaces spanned by a set of bases, to which a signal can be mapped for multi-resolution analysis. Such a characteristic allows WPT to be successfully applied to feature extraction in the fields of pattern recognition (14, 15, 18).

In this study, the WPT with the five-order symmlet wavelet was first applied to each channel of an analysis window for feature extraction. The five-order symmlet wavelet was selected from many mother wavelet functions frequently used in previous reports (10, 14, 15) and was further determined by some pretests in terms of classification performance. The WPT depth is also an important factor for WPT analysis. It is acknowledged that a small depth cannot yield sufficient resolution for extracting effective features, whereas a large depth leads to much more computational complexity. By considering this trade-off, the WPT depth of 3 or 4 has been recommended by previous studies (15, 19). The WPT depth of 4 was chosen in this study after some pretests, thus producing 30 subspaces in total. After the WPT, the energy values of all 30 subspaces were calculated as potential features (refer to feature selection approach in the following section), where the energy of each subspace was defined as a logarithmic value of the summation of the squares of all wavelet packet coefficients in the subspace. The logarithmic transform was chosen for showing better performance of classification after some tests.

## **Feature Selection Using Best Basis Selection**

The WPT binary tree yielded a redundant set of subspaces due to the subspace overlap in frequency axis. Afterward, the features extracted from all subspaces were regarded to carry redundant information. A great number of redundant features were likely to impose high computational cost and compromise classification performance. For application of the WPT analysis to feature extraction or pattern recognition, a best basis is usually chosen to maximize the class separability in terms of a proper discriminant measure. To achieve this goal, a feature selection procedure relying on a best basis selection algorithm is necessary. In this study, the algorithm was designed to choose the best set of subspaces from the WPT binary tree, since each subspace produced a potential feature. To determine the best subspace, Fisher's class separability index (FCSI) described in Ref. (20) was employed as the discriminant measure, which is introduced below.

Suppose that { *x* (*c*) *i,*(*j,k*) }*Nc i*=1 represents a set of energy features extracted from the subspace Ω*j*,*k* of the training signals belonging to class *c* (1 *≤ c ≤ C*, here *C* = 20), where *Nc* is the number of samples (i.e., analysis windows) in class *c*.

For each subspace, the mean and variance of these features grouped by class can be calculated as

$$\overline{m}\_{j,k}^{(\boldsymbol{\epsilon})} = \frac{1}{N\_{\boldsymbol{\epsilon}}} \sum\_{i=1}^{N\_{\boldsymbol{\epsilon}}} \left\{ \mathfrak{x}\_{i,(j,k)}^{\{\boldsymbol{\epsilon}\}} \right\}\_{i=1}^{N\_{\boldsymbol{\epsilon}}},\tag{1}$$

$$\max\_{1 \le i \le N\_\epsilon} \left( \mathfrak{x}\_{i,(j,k)}^{(\mathfrak{c})} \right) = \frac{1}{N\_\epsilon} \sum\_{i=1}^{N\_\epsilon} \left( \mathfrak{x}\_{i,(j,k)}^{(\mathfrak{c})} - \overline{m}\_{j,k}^{(\mathfrak{c})} \right)^2. \tag{2}$$

Here, the operator var*i*(*·*) is defined to calculate the variance of a set of constant variables indexed by *i*. Thus, the FCSI, for the subspace Ω*j*,*k*, is finally defined as

$$\text{FCSI} = \sum\_{p=1}^{K-1} \sum\_{q=p+1}^{K} \frac{\left| \overline{m}\_{j,k}^{(p)} - \overline{m}\_{j,k}^{(q)} \right|^2}{\text{var}\_{i \le i \le N\_p} \left( \boldsymbol{x}\_{i,(j,k)}^{(p)} \right) + \max\_{1 \le i \le N\_q} \left( \boldsymbol{x}\_{i,(j,k)}^{(q)} \right)}. \tag{3}$$

where *p* and *q* represent the indices of two different classes. Generally, a higher value of FCSI indicates higher degree of class separability. The best basis selection algorithm using FCSI is able to rank the features and make it practical to choose a subset of these regarded as being most discriminant.

In this study, feature selection approach was independently performed on each channel. Many previous studies regarding wavelet packet features also took the same procedure (10, 21, 22). For each channel, the number of selected subspaces/features needed to be carefully determined. It should be acknowledged that inadequate number of features may not guarantee the classification performance, whereas too many features lead to much computational cost. Considering such a trade-off, we set the number of subspaces/features per channel to 12 after performing sensitivity analysis (in terms of classification accuracy) by varying the feature number per channel from 1 to 25. Finally, the features from all channels were further concatenated as a high-dimensional feature vector for each analysis window.

## **Feature Dimensionality Reduction and Classification**

Even with the above feature selection procedure, the high-density surface EMG recordings still resulted in very high-dimensional feature vectors (i.e., 552-dimensional feature vectors with 12 best bases for each of 46 channels). In this case, feature dimensionality reduction is required to ensure the generalization capability of a classifier (23). In this study, uncorrelated linear discriminant analysis (ULDA) was used to reduce the feature dimension, which minimizes within-class distance and maximize between-class distance by an optimal transformation (24).

After the feature dimensionality reduction, linear discriminant classifier (LDC) was employed in this study. The LDC is able to model the within-class density of each class as a multi-variant Gaussian distribution and gives decisions of unknown samples by using the maximum a posteriori probability (MAP) rule and Bayesian principles (9, 25). The LDC was used due to its ease of implementation and efficient classification performance (4, 8).

In this study, the pattern classification was conducted in a user-specific manner, where both training dataset and testing dataset were derived from the same stroke subject. A fivefold cross-validation was conducted to evaluate the classification performance. This indicated that the EMG data from any four repetitions of muscle contraction were selected and assigned as training dataset, while the EMG data from the remaining repetition were used to form the testing dataset. The classification performance for each subject was evaluated as classification accuracy, which was calculated as the percentage of correctly classified windows over all the testing windows including all movement patterns over testing dataset. These window numbers were summed up over all fivefold tests for each subject.

For the performance comparison, the routine TD feature set including four statistics of the surface EMG signals, namely, mean absolute value (MAV), the number of zero crossing (ZC), the slope sign change (SSC), and the waveform length (WL), was also employed during the tests. The TD feature set was used in a similar way as previous studies that all TD features from all the considered channels were concatenated to form a feature vector for each analysis window. The same feature dimension reduction approach using ULDA was applied as well before LDC classifier implementation.

## **Channel Selection**

The use of FCSI for quantifying the discriminating power of features was further extended to channel selection from high-density surface EMG recordings. After the feature extraction and selection methods introduced above, a subset of features was determined for each channel and used to form a vector representing the most discriminable information from that channel. In order to perform channel selection, it was necessary to assess the discriminating power of feature vectors rather than scalars. Thus, the FCSI was accordingly modified as follows.

Here, let { **x** (*c*) *i,l* }*Nc i*=1 be a set of feature vectors extracted from the *l*-th channel of the training data belonging to class *c*. The mean of these feature vectors, originally defined in Eq. 1, needs to be modified, and their variance, namely var 1*≤i≤Nc* ( **x** (c) *i,l* ) , is further defined to be the summation of all variances calculated along any single dimension of the vector, as depicted in Eqs 4 and 5.

$$
\overline{\mathbf{m}}\_{l}^{(\boldsymbol{\epsilon})} = \frac{1}{N\_{\boldsymbol{\epsilon}}} \sum\_{i=1}^{N\_{\boldsymbol{\epsilon}}} \mathbf{x}\_{i,l}^{(\boldsymbol{\epsilon})},\tag{4}
$$

$$\begin{split} \mathop{\mathrm{var}}\limits\_{1\leq i\leq N\_{\epsilon}}\Big(\mathbf{x}\_{i,l}^{(\mathsf{c})}\Big) &= \mathop{\mathrm{var}}\limits\_{1\leq i\leq N\_{\epsilon}}\Big(\mathsf{x}1\_{i,l}^{(\mathsf{c})}\Big) + \mathop{\mathrm{var}}\limits\_{1\leq i\leq N\_{\epsilon}}\Big(\mathsf{x}2\_{i,l}^{(\mathsf{c})}\Big) \\ &+ \cdots + \mathop{\mathrm{var}}\limits\_{1\leq i\leq N\_{\epsilon}}\Big(\mathsf{x}d\_{i,l}^{(\mathsf{c})}\Big), \end{split} \tag{5}$$

where **x** = [*x*1, *x*2, *. . .*, *xd*] *T* denotes a *d*-dimensional vector. Thus, the FCSI, for the *l*-th channel, can be finally computed *via*

$$\text{FCSI} = \sum\_{p=1}^{K-1} \sum\_{q=p+1}^{K} \frac{\left| \overline{\mathbf{m}}\_{l}^{(p)} - \overline{\mathbf{m}}\_{l}^{(q)} \right|^{2}}{\text{var}\_{1 \le i \le N\_{p}} \left( \mathbf{x}\_{i,l}^{(p)} \right) + \max\_{1 \le i \le N\_{q}} \left( \mathbf{x}\_{i,l}^{(q)} \right)}, \quad \text{(6)}$$

where *p* and *q* represent the indices of two different classes again. Similarly, a higher FCSI value indicates a higher degree of class separability for a certain channel. Following the strategy of feature selection using FCSI, a subset of optimal channels can be selected by ranking the channels using FCSI. This channel selection approach was termed as FCSI method in this study.

Channel selection has also been conducted in previous studies (25, 26) to assess the myoelectric pattern recognition performance using a reduced number of EMG channels selected from highdensity signal recordings. A straightforward algorithm, termed as sequential feedforward selection (SFS), was often used, which iteratively adds the most informative channels in terms of classification accuracy. In the first iteration of this algorithm, each of all candidate channels is independently used and the channel producing the highest classification accuracy was selected to be the first optimal channel. During the next iteration, the previously selected channels were combined with each of the other channels to form a new subset sequentially, and the subset producing the highest classification accuracy was determined. This procedure can be iteratively performed when meeting a desired number of selected EMG channels. Note that the SFS directly uses the classification accuracy as the criterion, which conventionally requires classifier training and testing procedures in each iteration. Thus, the channels selected by the SFS algorithm are more likely to be overfitted to the testing data with limited generalization ability. In order to avoid such overestimated performance in some degree, the SFS algorithm used in this study was performed only on the training dataset. This required the original training dataset consisting of four repetitions of muscle contraction to be further divided into two parts, one consisting of three repetitions for SFS training and the other consisting of the remaining repetition for SFS testing. To evaluate the classification performance with the channels selected by the SFS algorithm, a classifier was implemented with all the four repetitions (used for SFS) as training dataset and the remaining fifth repetition (which was not used for SFS) as testing dataset.

The channel selection using FCSI is able to independently choose a subset of best channels in any size *m*. It should be acknowledged that the *m* best channels may not be the best *m* channels. By contrast, the standard SFS algorithm offers a practical way of selecting a subset of appropriate channels by taking the effect of channel combination into account, but it conventionally suffers from the overfitting problem. By taking advantage of both methods to overcome its own drawbacks, a novel channel selection method named FCSI + SFS was proposed in this study. For clarity, the FCSI + SFS algorithm can be briefly described as follows:

Consequently, the performance of the proposed FCSI and FCSI + SFS algorithms was examined and compared with solely using the SFS algorithm for channel selection. To ensure a fair comparison, all the three algorithms selected their respective desired channels using the training dataset (i.e., four repetitions), while the classification performance of the selected channels was evaluated using the fifth one (not involved in channel selection process) as testing dataset for the classifier.

## **RESULTS**

## **Feature Selection and Classification**

An example of the effectiveness of FCSI for quantifying the discriminating power of features is shown in **Figure 2**, where the distribution of features for three representative classes were demonstrated in three scatter plots: (a) with the lowest FCSI values, (b) with the highest FCSI values, and (c) from three TD parameters (WL, ZC, and SSC). From the visual inspection, it can be found that the features determined by three highest FCSI values reflect good class separability in the figure, whereas such separability was not observed for features with lowest FCSI values or three TD features.

Following the wavelet packet feature extraction and selection using FCSI, along with LDC classification, pattern recognition analysis was implemented in a user-specific manner for all 12 stroke subjects. **Table 2** summarizes the classification performance in terms of overall accuracy for identifying 20 intended upper limb movement, when both the WPT-based method and TD feature extraction method were applied to the EMG data consisting of 46 high-density channels or 10 predefined channels, respectively. A two-way repeated-measure ANOVA was applied on the classification accuracies, with the channel number (highdensity 46 and 10) and feature set (WPT and TD) both considered as within-subject factors, in order to examine their effect. It can be unsurprisingly observed that high classification accuracies above 95% were achieved for almost all subjects when the 46 highdensity channels were totally used, regardless of the feature extraction methods. By contrast, the use of predefined 10 channels led to a performance compromise with an averaged accuracy of 91.15% for the TD features and 92.91% for the WPT features, respectively. An overall significant effect of both channel number (*F* = 14.597, *p* = 0.003) and feature set (*F* = 10.031, *p* = 0.009) on classification accuracy was revealed by the ANOVA. In this case, the WPTbased feature extraction approach showed superior performance to the routine TD feature extraction method by about 2% accuracy improvement with statistical significance.

## **Channel Selection**

The performance of the proposed method for selecting an appropriate subset of channels was further examined. Admittedly, the performance of myoelectric pattern recognition is sensitive to both the channel number and the number of extracted features per channel. By changing both factors, their effect on the classification performance was simultaneously examined using the FCSI algorithm. **Figure 3** shows a representative example from Subject 2 illustrating how the classification performance (as described by error rate) changes in the extracted/selected feature number per channel varying from 1 to 25 and the channel number varying from 1 to 20. It can be observed that very low (approximately 1 or 2) feature number per channel or channel number

**TABLE 2 | Classification accuracy (unit: %) in stroke subjects when both TD and WPT features were extracted from the EMG data of 46 high-density channels and 10 predefined channels, respectively**.

*TD, the time domain feature set; WPT, the proposed feature set using wavelet packet transform.*

could not produce high classification performance (low error rate) and the increase of channel number played a critical role in performance improvement. Similar findings were observed for the other subjects. Considering the trade-off between classification performance and practicability (i.e., low computational cost and reduced number of channels), the feature number was set to be 12, producing an error rate of 1.22% for Subject 2 when the channel number was reduced to 7. This confirmed our setting of feature number per channel to 12 in both previous and following data analyses.

After the feature number per channel was appropriately determined, the performance of three channel selection algorithms was evaluated. **Figure 4** reports the classification accuracies averaged across 12 stroke subjects, when the EMG channels were progressively selected using FCSI, SFS, and FCSI + SFS, respectively. When applying WPT and TD feature extraction methods on 10 predefined channels, in addition, the achieved classification accuracies are indicated as two horizontal dashed lines in **Figure 4** for comparison purpose. It can be found that each of the three algorithms yielded a similar increasing trend of classification accuracy when the channel number increased. The classification accuracy increased rapidly to approximately or over 90% at channel number ranging from 1 to 10 and then remained almost steady or slightly increased with further channel number increasing. The proposed FCSI + SFS method demonstrated its superior performance to the other two with highest average accuracies. Specifically, by using 10 optimally selected channels as compared with the 10 predefined channels, improved classification performance was obtained. Meanwhile, the use of only 5 channels optimally selected by either FCSI + SFS or SFS algorithm was found to produce classification performance comparable to that of using 10 predefined channels. Furthermore, a series of bivariate Pearson's correlation analyses were conducted to further examine the effect of subjects' clinical information (including years post stroke, FMUE, and C–M hand scores) on the classification accuracies derived from the use of any channel number (high-density 46, predefined 10, or optimally selected 10 channels by FCSI\_SFS or SFS) along with any feature set (WPT or TD), respectively. No significant correlation was found between any clinical information and the classification accuracy (*p >* 0.058) except the correlation between the FMUE score and the classification performance with WPT feature extracted from 10 predefined channels (correlation coefficient *R* = 0.651, *p* = 0.022).

**Table 3** shows the first 10 selected channels for each subject using the 3 methods. It was found, as would be expected, that the selected channels were different across subjects even using the same method. For each subject, the selected channels also varied when three methods were performed, respectively. However, for each subject, several channels (marked in bold numbers, though, with varying order of selection) were commonly selected using any of the three algorithms.

## **DISCUSSION**

Myoelectric pattern recognition has great potential for implementing interactive control of assistive robotic devices, which is of particular importance for restoration of dexterous arm/hand functions. Previous high-density surface EMG pattern recognition analysis using conventional TD or AR + RMS features has revealed that substantial neural control information can be readily extracted from paretic muscles of stroke patients. In the current study, a feature extraction method based on WPT was reexamined and applied to high-density surface EMG signals from stroke subjects for improved myoelectric pattern-recognition performance. Taking advantage of the classic wavelet packet feature extraction and selection approach, a novel channel selection

SD error bars. The classification accuracies derived from applying both WPT and TD features to 10 predefined channels are indicated as two horizontal dashed lines.

*The bold numbers represent commonly selected channels using any of the three methods for each subject.*

method was furthermore developed to determine a practical number of appropriate EMG channels for maintaining high classification accuracies, an issue particularly important for implementing a practical myoelectric control system.

The FCSI was used in this study to quantify the discriminating power of each feature or wavelet packet basis/subspace where the feature was derived. There have been different algorithms or criteria for determining the best basis or subspace in WPT analysis (15, 20). For pattern recognition analysis, the adopted criterion is preferably associated with class separability. The FCSI is such a criterion that is able to measure the class separability of a feature or feature vector, more specifically, in almost the same way as the LDC classifier does. The FCSI was used to determine the most discriminating features from those produced by WPT analysis in various time–frequency scales. Due to the advantages of time–frequency resolution provided by the WPT as well as the FCSI analysis, the wavelet packet feature extraction and selection approaches demonstrated improved performance, as compared with the previously used conventional time domain or frequency domain feature sets, especially for subjects with relatively high levels of impairments. For example, 5 of 12 stroke subjects (i.e., Subjects 1, 2, 3, 4, and 9) produced relatively low classification accuracies below 95.0%, respectively, when the TD feature set was applied, whereas improved accuracies above 95% (**Table 2**) were achieved for these subjects using the wavelet packet features. It is worth noting that the TD and AR features were often employed for myoelectric pattern recognition for amputee subjects toward prosthesis control, which can achieve comparable performance to more complicated features including wavelet packet features (8, 9, 25). In contrast, the advantages of wavelet packet feature extraction and selection appeared more evident in processing stroke data, presumably due to the fact that the residual muscles of an amputee subject are neurologically intact, whereas the paretic muscles of stroke subjects usually suffer from different symptoms such as weakness, spasticity, etc. Due to the fact that neural control information delivery is hampered by injuries to neuromuscular pathways after stroke, more complicated features (e.g., WPT features) are likely to emerge their advantage in characterizing such paretic EMG signals, which was demonstrated in this study.

The FCSI used for WPT feature selection was further extended for channel selection, in combination with the SFS method. The combined FCSI + SFS method demonstrated superior performance for channel selection in terms of classification performance than solely using the SFS or the FCSI method. Using the FCSI rather than the direct classification accuracy in combination with the SFS algorithm avoids repeated training and testing of a classifier (required for each iteration). Furthermore, in this study, the channel selection procedure and the performance testing procedure were not based on the same datasets in order to overcome the overfitting problem. Besides, when the same number (e.g., 10) of channels were employed, the channels selected *via* an optimal algorithm (e.g., FCSI + SFS) yielded higher classification accuracies across all 12 stroke subjects than those predefined electrode sites. In addition, with the WPT feature set, the correlation analyses revealed dependence of the classification performance on the FMUE score with statistical significance (*p* = 0.021) when a set of predefined 10 channels were used. Such dependence disappeared when high-density 46 channels or optimally selected 10 channels were adopted (*p >* 0.021), indicating that those subjects with relatively high levels of impairments (i.e., low clinical assessment scores) had substantial improvement of classification performance. The channel selection analysis not only confirms previous findings in Ref. (4, 27) that it is feasible to use a small number of EMG channels (rather than a high-density electrode array) for decoding sufficient neural control information from paretic muscles but also indicates the necessity of determining appropriate control site locations (rather than predefined channels) for improved classification performance. Therefore, effective algorithms, such as the FCSI + SFS, reported in this study are of critical demand for developing a practical myoelectric control system, particularly for stroke users.

When examining the selected channel index, it was found that the selected channels were different among 12 stroke subjects even using the same channel selection method, primarily due to individual subject difference following stroke (such as impairment nature and level, recovery status, daily activity, etc.). It confirms our previous suggestion (4) that the myoelectric pattern recognition should be designed or conducted in a user-specific manner. The designed system may include appropriate EMG features and channels (e.g., electrode number, location, configuration, etc.) that maximize the classification accuracy to enhance its usability for stroke subjects with any impairment level, while its suitability for real time application (such as computational cost, adaptability to slight electrode movement, etc.) should also be considered. We acknowledge that the examined WPT-based feature extraction and selection approach may induce relatively higher computational complexity than using conventional TD feature set. Even so, the WPT method is still very practical for real-time implementation demonstrated by an enormous number of previous studies (10, 14, 28). Also, the choice of the target movements or controlled function should consider subject need and classification performance. Although high-density surface EMG recording contains much redundant information for myoelectric pattern recognition analysis, it provides a very useful and essential way to optimize the myoelectic control system designed for individual stroke patients. In this regard, the high-density sEMG recording, along with effective channel selection, can be designed as a necessary calibration procedure. Such a procedure is recommended to be conducted just once, rather than regularly, during the prescription of the myoelectrically controlled robotic training for stroke patients with different impairment levels.

## **CONCLUSION**

In this study, a feature extraction method based on WPT was applied to myoelectric pattern recognition analysis in stroke survivors. By processing high-density surface EMG recordings from paretic muscles of 12 stroke subjects, the WPT features achieved an improved performance for classification of 20 different arm/hand movements compared with the conventional TD EMG features. Furthermore, a novel channel selection method was developed by combining the FCSI and the SFS analyses, which can effectively determine a small number of appropriate EMG channels without significantly compromising the classification performance achieved from high-density surface EMG. These novel feature extraction and channel selection analyses confirm substantial neural control information available in paretic muscles of stroke survivors, and moreover, demonstrate the feasibility of extracting such information with a practical number of EMG channels. The findings are helpful for development of myoelectric control systems for stroke rehabilitation.

## **AUTHOR CONTRIBUTIONS**

DW analyzed the data, interpreted the results, and wrote the first draft of the manuscript. XZ designed the study and performed all stages of the study including data collection, analysis, interpretation, and substantial revision of the manuscript. XG, XC, and PZ participated in data analysis and interpretation and revised the manuscript. All the authors approved the final version of the manuscript.

## **FUNDING**

This work was supported in part by the National Natural Science Foundation of China under Grant 61401421 and the Fundamental Research Funds for the Central Universities under Grant WK2100230014.

## **REFERENCES**

**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 Wang, Zhang, Gao, Chen and Zhou. 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.*