Improving Myoelectric Control for Amputees through Transcranial Direct Current Stimulation.

This article hasThis beenisaccepted the author's for version publication of aninarticle a future that issue has of been thispublished journal, but in this has journal. not beenChanges fully edited. wereContent made tomay this change versionprior by the to publisher final publication. prior to publication. Citation information: DOI 10.1109/TBME.2015.2407491, The final version of record isIEEE available Transactions at http://dx.doi.org/10.1109/TBME.2015.2407491 on Biomedical Engineering 1

Improving Myoelectric Control for Amputees through Transcranial Direct Current Stimulation Lizhi Pan, Student Member, IEEE, Dingguo Zhang*, Senior Member, IEEE, Xinjun Sheng, Member, IEEE and Xiangyang Zhu, Member, IEEE

Abstract—Most prosthetic myoelectric control studies have shown good performance for unimpaired subjects. However, performance is generally unacceptable for amputees. The primary problem is the poor quality of electromyography (EMG) signals of amputees compared with healthy individuals. To improve clinical performance of myoelectric control, this present study explored transcranial direct current stimulation (tDCS) to modulate brain activity and enhance EMG quality. We tested six unilateral transradial amputees by applying active and sham anodal tDCS separately on two different days. Surface EMG signals were acquired from the affected and intact sides for eleven hand and wrist motions in the pre-tDCS and post-tDCS sessions. Autoregression (AR) coefficients and linear discriminant analysis (LDA) classifiers were used to process the EMG data for pattern recognition of the eleven motions. For the affected side, active anodal tDCS significantly reduced the average classification error rate (CER) by 10.1%, while sham tDCS had no such effect. For the intact side, the average CER did not change on the day of sham tDCS but increased on the day of active tDCS. These results demonstrated that tDCS could modulate brain function and improve EMG-based classification performance for amputees. It has great potential in dramatically reducing the length of learning process of amputees for effectively using myoelectricallycontrolled multifunctional prostheses. Index Terms—Electromyography (EMG), Myoelectric control, Pattern recognition, Transcranial direct current stimulation (tDCS), Transradial amputee

I. I NTRODUCTION URFACE electromyography (EMG) signals have long been used as control input for myoelectric prostheses [1], [2], [3], [4], and pattern recognition methods have been employed to classify EMG signals towards multifunctional prosthesis control for more than 20 years [5], [6], [7]. Though the classification accuracy of intact-limb subjects can be as high as 95% [8], [9], [10], clinical use and commercial impact of multifunctional prostheses are still limited [11]. In general, amputees cannot produce consistent and distinguishable muscle activity patterns as intact-limb subjects, which are essential

S

Copyright (c) 2014 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending an email to [email protected]. This work was supported by the National Basic Research Program (973 Program) of China (No. 2011CB013305), the National Natural Science Foundation of China (No. 51475292), and the Science and Technology Commission of Shanghai Municipality (No. 11JC1406000). Asterisk indicates the corresponding author. Lizhi Pan, Dingguo Zhang*, Xinjun Sheng and Xiangyang Zhu are with the State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China (E-mail: [email protected], [email protected], [email protected], [email protected]).

for robust pattern recognition-based control of prostheses [12]. Consequently, the number of motions that can be reliably classified is limited for amputees, and they require a long and frustrating learning phase before they can effectively use the devices. This is a primary reason for the low retention rate of multifunctional prostheses [13], [14]. Several studies demonstrated that repetitive exercise and training could both improve the motor function of transradial amputees and decrease classification error rate (CER) for EMG pattern recognition. Kato et al. increased the number of classes that could be identified from 3 to 6 (CER was less than 20%) [15]. Powell et al. demonstrated that three weeks of exercise could reduce CER from 22.5% to 5.6% with as many as 8 motion classes [12]. However, repetitive exercise and long-term training often frustrate the user, leading to frequent device abandonment. Thus, alternative novel methods, which can facilitate user learning and reduce length and complexity of the training phase, are imperative for better user compliance of myoelectrically-controlled prostheses. Recently, noninvasive brain stimulation has become one of the most promising techniques for neuromodulation. These techniques include transcranial magnetic stimulation (TMS) [16], transcranial focused ultrasound stimulation (tFUS) [17] and transcranial electrical stimulation (tES) [18], [19]. These three methods can be used to modulate different cortical areas, changing the associated neurophysiological signals. Transcranial direct current stimulation (tDCS), a type of tES, is widely used by researchers due to its low cost, versatility and portability [20], [21], [22]. In tDCS, a small constant direct current is delivered to the brain through large pad electrodes placed on the scalp [23]. Some research suggested that direct current flow changed excitability of neurons in the brain by altering resting membrane potential [24], [25], i.e. priming the corresponding cortex area. Nitsche et al. demonstrated that anodal stimulation increased motor cortex excitability (facilitation) while cathodal stimulation decreased excitability (inhibition) [26]. Hummel et al. and Fregni et al. showed that anodal tDCS of stroke patients’ lesioned primary motor cortex improved distal motor function measured by the Jebsen Taylor Hand Function Test (JTT) [27], [28], [29]. A pilot study by Boggio et al. provided evidence of a positive effect of anodal tDCS on the enhancement of non-dominant hand motor function in healthy subjects [30]. These subjects used their hands asymmetrically in daily life. As a result, there was a lateral imbalance of motor cortical excitability between the dominant and non-dominant hands. A similar situation exists for unilateral transradial am-

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putees: the affected side is not used at all, thereby decreasing the excitability of corresponding motor cortex with respect to the intact side. This results in a lateral imbalance of the motor cortical excitability of amputees that is more pronounced than that of the healthy subjects’ hand dominance. Reilly et al. demonstrated that different movement intentions of the phantom limb could activate different phantom movement cortical representations, which would produce distinct EMG patterns in stump muscles [31]. They further showed a positive correlation between a reduction of voluntary accessibility to phantom limb movement representations and time from amputation, thereby resulting in a “frozen” phantom limb in some extreme cases. We hypothesize that the modulation of the amputee’s motor cortex, through noninvasive cortical stimulation, such as tDCS, will facilitate “awakening” the “frozen” phantom limb movement representations, accelerate the relearning process of motor tasks, and consequently improve the quality of the EMG signals, i.e. the consistency of the same motion and the distinctiveness of different motions. As a result, the user training phase for a myoelectrically-controlled prosthesis can be significantly shortened and its complexity is reduced. Dutta et al. recently presented a study on facilitating myoelectric control in healthy subjects with tDCS for triggering functional electrical stimulation [32], which improved on-off detection performance from a single muscle. This is, however, different from myoelectric control for multifunctional prostheses, which requires coordinating activations of multiple muscles. Krishnan et al. demonstrated that elbow flexor muscle recruitment strategies could be altered by anodal tDCS [33]. They suggested that tDCS could be used as a potentially adjuvant therapy for the treatment of muscle weakness and activation failure. In this study, we investigate whether anodal tDCS of the contralateral primary motor cortex can improve the myoelectric control performance for eleven classes of phantom hand and wrist motions. The proposed experiments are conducted on six unilateral transradial amputees to test the effects of anodal tDCS. Autoregression (AR) coefficients and linear discriminant analysis (LDA) classifiers are used to process EMG data. A preliminary version of this work on three able-bodied subjects and one transradial amputee has been previously reported [34].

II. M ETHODS A. Subjects Six unilateral transradial amputees (three males and three females; aged 36-72; referenced as Sub1-Sub6) participated in this experiment (Table I). None of the subjects had prior tDCS experience. The subjects had neither neurological disorders, nor any contraindication to tDCS. The subjects were not informed about the positive effects of tDCS in order to avoid the placebo effect. This work was approved by the Ethics Committee of Shanghai Jiao Tong University. All subjects signed informed consent and testing procedures were in compliance with the Declaration of Helsinki.

Fig. 1. Schematic of the experiment design showing the random allocation of the two days with active and sham tDCS. Total time for the pre-tDCS and the post-tDCS sessions was approximately 60 minutes and total time for the tDCS session was 20 minutes.

B. Experiment Setup The subjects underwent two interventions: active and sham anodal tDCS of the primary motor cortex corresponding to the affected side. They participated in experiments on three days, with one specific task on each day. On the first day, there was no tDCS intervention session. The subjects were introduced to the myoelectric experiment environment, and familiarized themselves with the experimental protocol. For the myoelectric control training procedure, the subjects were instructed to perform motions using either the affected side or the intact side the same way as in the pre-tDCS sessions and the post-tDCS sessions of the following two days. After training and exercising, the myoelectric performance of the subjects became stable. On the second and third days, the subjects were randomized to receive either active anodal tDCS intervention or sham tDCS intervention. There was an interval of 48 hours between the latter two experiment days, each of which included 3 sessions: pre-tDCS myoelectric control session, tDCS session (sham or active) and post-tDCS myoelectric control session (Fig. 1). In the pre-tDCS sessions and the post-tDCS sessions, the subjects were instructed to perform motions using either the affected side or the intact side. The number of the affected side of the subjects was equally distributed on the left and the right (Table I). Thus, performing motions in sequence of the left and the right sides ensured that both the affected and the intact sides would be randomly allocated. The “mirrored bilateral motion” strategy was not used, because the representation of contralateral limb motion would invade the primary motor cortex corresponding to the affected side [35], [36], [37], which could result in unexpected artifacts on the performance of tDCS. Independent side motions were necessary to exclude



TABLE I S UMMARY OF THE CHARACTERISTICS OF THE SUBJECTS Subject (gender, age)

Dominant hand

Affected side

Residual stump length

Cause of amputation

Time since amputation

Daily prosthesis usage /type of prosthesis

Sub1 Sub2 Sub3 Sub4 Sub5 Sub6

Right Right Right Right Right Right

Right Left Right Left Left Right

15 cm 10 cm 8 cm 17 cm 16 cm 16 cm

Traumatic Traumatic Traumatic Traumatic Traumatic Traumatic

34 years 25 years 31 years 30 years 7 years 8 years

Half day, myoelectric Half day, myoelectric All day, cosmetic Half day, cosmetic Half day, cosmetic Half day, myoelectric

(M, 72) (F, 50) (F, 56) (F, 57) (M, 60) (M, 36)

hand hand hand hand hand hand

this effect on the primary motor cortex corresponding to the affected side. Thus, the subjects did not obtain help when performing “mirrored bilateral motion”. Eleven classes of hand and wrist motions were performed in the following order: hand close (HC), hand open (HO), key grip (KG), tip prehension (TP), wrist flexion (WF), wrist extension (WE), radial deviation (RD), ulnar deviation (UD), forearm supination (FS), forearm pronation (FP) and “no movement” (NM). In each trial, the subjects were asked to perform each motion for 10 seconds. Ten trials were performed for each side in both of the two sessions. To avoid fatigue, the subjects rested between each trial. In the tDCS sessions, the subjects underwent 1mA active or sham anodal tDCS for 20 minutes by using a DCStimulator (NeuroConn Inc., Germany). A 5cm×7cm salinesoaked sponge anodal electrode was placed over C3 or C4 (international 10/20 EEG system) and the cathodal electrode was placed over the contralateral supraorbital area (Fig. 2(a)). It should be noted that C3 or C4 was chosen depending on the affected side. For the sham stimulation, the electrodes were placed on the same positions; however, the current was ramped up over 10 seconds, held constant at 1mA for only 10 seconds (not the full 20 minutes), and then ramped down to zero over 10 seconds to provide blinding effects.

(a)

C. Data Acquisition EMG signals were acquired by a Trigno wireless system (Delsys Inc., USA). Four-channel EMG signals were recorded from four forearm muscles of each side (Fig. 2(b)): 1. flexor carpi ulnaris (FCU), 2. flexor carpi radialis (FCR), 3. extensor carpi radialis (ECR), 4. extensor carpi ulnaris (ECU). The skin surface of the areas of interest was rubbed lightly with alcohol to reduce impedance. All electrodes were mounted over the targeted muscles using medical adhesive tapes. The electrodes were wirelessly connected to the Trigno Base Station, which communicated with a computer through a USB cable. The EMG signals were sampled at 2000 Hz and band-pass filtered (pass band 20-450 Hz). D. Data Processing Feature extraction is a necessary step for pattern recognition-based myoelectric control. As the AR features have been shown to be efficient and effective in previous myoelectric control studies [1], [38], [39], sixth-order AR coefficients were used in this work. Here, the length of analysis window was set to 200 ms and the increment of two adjacent

(b) Fig. 2. Experiment setup. (a) positions of the tDCS electrodes on the scalp of Sub2 from the top view; (b) positions of the EMG electrodes on the affected and intact sides of Sub2. The amputee’s left hand was lost.

windows was set to 50 ms. The feature set was computed on each of the four channels of one side, and then concatenated to form a 24-dimension feature vector. The LDA classifier has been widely used for pattern recognition of EMG signals [7], [8]. It is widely accepted that the LDA classifier can perform comparably with more sophisticated classifiers [40] and generalizes better than nonlinear classifiers in the presence of electrode shift [41]. Hence, the LDA classifier was used in this study. A two-fold crossvalidation procedure was used. One half of the data were randomly selected and used as a training set to train the LDA classifier, while the remaining half were used as a testing set.



E. Quantification of Feature Space

G. Statistical Analysis

The mean semi-principal axis (MSA) and the separability index (SI), proposed by [42], were applied to quantify variations in EMG signals in the feature space between the pretDCS and post-tDCS sessions. MSA is defined to measure the size of the hyperellipsoid, as a mean value of the geometric mean of the semi-principal axes of the hyperellipsoids across N motion classes:

A four-way repeated measures ANOVA was used to analyze CER. The ANOVA included the following four factors: Side (affected/intact), Stimulation (tDCS/sham), Time (pre/post), and Motion (11 classes). Prior to the statistical analysis, Fisher’s transformation was applied on the values of CER/100 to normalize the data. Further, a three-way repeated measures ANOVA was used to analyze MSA and SI. The three factors were: Side (affected/intact), Stimulation (tDCS/sham), and Time (pre/post). Similarly, a three-way repeated measures ANOVA was used to analyze ST. The three factors were: Side (affected/intact), Stimulation (tDCS/sham), and Motion (10 classes of active motions). In all ANOVA tests, the full model was conducted first. When a significant interaction was detected, a simple-effects analysis was conducted by fixing the levels of one of the interacting factors. When no interaction was detected, a reduced ANOVA model with only main factors was performed. Only a significant difference was reported for these statistical analyses. The significance level for all tests was p = 0.05.

1/D

D N 1 ∑ ∏ (( ajk ) M SA = N j=1

)

(1)

k=1

where N is the number of motion classes (here it is 11), D is the dimension of the feature vector (here it is 24), and ajk is the length of semi-principal axes corresponding to the kth principal component of class j. The value of MSA is positively related to intra-class dispersion. SI is defined to measure the diversity of different motion classes, as a mean value of one-half of the Mahalanobis distance from the centroid of the ellipsoid of class j to the centroid of the ellipsoid of the nearest class i across N motion classes:

SI =

N √ 1 ∑ 1 T (µi − µj ) Sj−1 (µi − µj )) (min N j=1 i̸=j 2

(2)

where µj and µi are the centroid of the ellipsoid of class j and class i, and Sj is the covariance of the data for class j. The value of SI is positively correlated with inter-class distance. F. Quantification of Stability of EMG signals Our hypothesis was that the stability of EMG signals should be improved after the intervention of active anodal tDCS. To validate this hypothesis, changes in the variance of EMG signals of the active motions between the pre-tDCS and posttDCS sessions on both the day of active tDCS and the day of sham tDCS were examined for both the affected and the intact sides. Stability index (ST) was defined as the ratio between mean standard deviation of the activation value across the channels of the pre-tDCS sessions and that of the post-tDCS sessions:

1 4

STi = 1 4

√

4 ∑ k=1 4 ∑

k=1

1 9

√ 1 9

10 ∑

(mi,j,k,pre −

j=1 10 ∑

j=1

(mi,j,k,post −

1 10

1 10

10 ∑

mi,j,k,pre )2

j=1 10 ∑

(3) mi,j,k,post

)2

j=1

where mi,j,k,pre is the activation value from motion i, trial j, channel k of the pre-tDCS sessions and mi,j,k,post is the activation value from motion i, trial j, channel k of the posttDCS sessions. If ST is larger than 1, it indicates that EMG signals of the post-tDCS sessions are more consistent than that of the pre-tDCS sessions.

III. RESULTS The CER results were achieved through AR feature extraction and LDA classification on EMG signals. The feature space was quantified by MSA and SI indexes, and the stability of EMG signals was quantified by ST index. The statistical method based on ANOVA was used to analyze the results. A. Classification Error Rate of Affected and Intact Sides Figure 3 shows the CER of the affected side of the subjects in different sessions. As shown in Fig. 3, the average CER of the affected side across the subjects was significantly reduced after active anodal tDCS by 10.1±2.6% on the day of active tDCS (p < 0.001). Consistent reduction was found for every subject. The greatest improvement was found for Sub3 (13.5%), and the smallest improvement was found for Sub2 (7.1%). On the day of sham tDCS, however, no significant change was found between the average CER of the pre-tDCS and post-tDCS sessions. The average change from the pretDCS session to the post-tDCS session was 0.7% (increased after the sham tDCS session). Figure 4 shows the CER of the intact side of the subjects in different sessions. As shown in Fig. 4, the average CER of the intact side changed from 6.2% to 6.9% on the day of sham tDCS, and changed from 5.6% to 8.8% on the day of active tDCS. To further investigate the individual contribution of each motion on the average CER reduction, Table II shows the CER of each motion of the affected side of the subjects on the day of active tDCS. Greater improvements are highlighted with bold text (the improvements larger than 13%). The average CER reduction of the hand motions and the wrist motions was 15.6% and 7.9%, respectively. The four-way ANOVA on CER found a statistically significant interaction among Side, Stimulation, and Time (p < 0.001). Since Motion did not interact with other three factors, and we were not particularly interested in the effects on CER



of different motions, we averaged the CER across different motions, leaving only three factors (Side, Stimulation, and Time). In the subsequent three-way ANOVA, we found the interaction of the three factors significant (p = 0.004), which prompted us to use a simple-effects analysis to break down the ANOVA further into subsequent two-way ANOVA, looking separately at the affected side and the intact side for main effects and interaction between Stimulation and Time. For the affected side, the two-way ANOVA revealed a statistically significant interaction between Stimulation and Time (p < 0.001), which prompted us to use a simple-effects analysis to break down the ANOVA further into subsequent one-way ANOVA, looking separately at the active tDCS and the sham tDCS for main effect of Time. For the active tDCS, the one-way ANOVA found that the CER of the post-tDCS session (17.2±3.0%) was significantly lower (p < 0.001) than that of the pre-tDCS session (27.3±3.5%). For the sham tDCS, the one-way ANOVA revealed no statistically significant main effect of Time (p = 0.45). The CER of the pre-tDCS and posttDCS sessions was 24.3±3.9% and 25.0±3.9%, respectively. For the intact side, the two-way ANOVA revealed a small but statistically significant interaction between the two factors (p = 0.048), which prompted us to use a simple-effects analysis to break down the ANOVA further into subsequent one-way ANOVA, looking separately at the active tDCS and the sham tDCS for main effect of Time. For the active tDCS, the one-way ANOVA found that the CER of the post-tDCS session (8.8±1.4%) was significantly higher (p = 0.004) than that of the pre-tDCS session (5.6±1.2%). For the sham tDCS, the one-way ANOVA revealed no statistically significant main effect of Time (p = 0.396). The CER of the pre-tDCS and post-tDCS sessions was 6.2±1.4% and 6.9±1.6%, respectively. B. Variations in EMG Feature Space between Pre-tDCS and Post-tDCS Sessions To understand the sources of the improvement in the above classification, the MSA and SI of the affected and intact sides for each subject in the pre-tDCS and post-tDCS sessions on the day of the active and sham tDCS were calculated. Figure 5 shows the MSA and SI of the affected side for each subject in the pre-tDCS and post-tDCS sessions on the day of the active and sham tDCS. Except for Sub2, active tDCS decreased the MSA for all subjects (Fig. 5 (a)). The smallest MSA was 0.057 in the post-tDCS session on the day of active tDCS for Sub1. Further, active tDCS increased the SI for all subjects (Fig. 5 (b)). The biggest SI was 2.40 in the post-tDCS session on the day of active tDCS for Sub1. Comparing the average MSA of the affected side in the pre-tDCS session with that in the post-tDCS session on the day of active tDCS (Fig. 5 (a)), active tDCS decreased the MSA of the feature vector, however no statistical difference was found, possibly due to the small sample size. Comparing the average SI of the feature vector of the affected side in the pre-tDCS session with that in the post-tDCS session on the day of active tDCS (Fig. 5 (b)), active tDCS increased the SI of the feature vector, however no statistical difference was found, possibly due to the small sample size.

For MSA, the three-way ANOVA revealed a main effect of Side (p < 0.001). No other statistically significant three-way, two-way interactions, or main effects were revealed. For SI, the three-way ANOVA revealed a main effect of Side (p < 0.001). No other statistically significant three-way, two-way interactions, or main effects were revealed. The ANOVA for MSA and SI revealed no statistically significant three-way, two-way interactions, nor main effects with the exception of the main effect of Side. The results of the above ANOVA suggested that the EMG signals of the affected side and the intact side were significantly different in the feature space. C. Stability Analysis of EMG Signals The ST of the active motions of the affected and intact sides for each subject on the day of the active and sham tDCS were calculated. Table III shows the ST of each motion of the affected side of the subjects on the day of active tDCS. Most ST values were greater than 1, which indicated smaller variance in the magnitudes of activation pattern vectors in the post-tDCS session. The STs highlighted with bold text were the motions corresponding to the greater improvements in Table II. Except for HO and UD of Sub1, HC of Sub3, FP of Sub5 and HC of Sub6, the STs highlighted with bold text were over 1 (the STs of HO and WE for Sub5 even reached 3.43 and 6.19), which indicated the greater improvements of these motions were induced by the more stable EMG signals after the active anodal tDCS intervention. Since the average ST of each motion across the subjects was greater than 1, generally, the amplitudes of the EMG signals of the post-tDCS session were more stable than that of the pre-tDCS session. Thus, it appeared that the stability of the EMG signals increased after the active anodal tDCS intervention. The three-way ANOVA on ST revealed a statistically significant two-way interaction between Side and Stimulation (p = 0.004). A statistically significant main effect of Side was also revealed (p = 0.002). No other statistically significant three-way, two-way interactions, or main effects were revealed. Subsequently, we used a simple-effects analysis to break down the ANOVA further into subsequent two-way ANOVA, looking separately at the affected side and the intact side for main effects and interaction between Stimulation and Motion. For the affected side, the two-way ANOVA revealed no two-way interaction (p = 0.877). But, a main effect of Stimulation was found to be statistically significant (p = 0.010). The twoway ANOVA showed that the STs of the active motions of the affected side on the day of active tDCS were significantly higher than that on the day of sham tDCS (p = 0.010). For the intact side, the two-way ANOVA revealed no statistically significant two-way interaction or main effects. The results of the above ANOVA suggested that the increase in ST was due to the stimulation mode, not due to the training effects. Unlike the affected side, the significant deterioration of the classification performance of the intact side was not reflected on the ST. The main reason was likely that the increase in CER of the intact side on the day of active tDCS was only 3.2%, which was much smaller compared to the reduction (10.1%) of the affected side on the day of active tDCS.



Fig. 3. CER of the affected side of the subjects. The error bar for each subject represents the standard deviation. The rightmost four bars are the average values across all subjects. Error bars for the rightmost four bars represent the standard error.

TABLE II C LASSIFICATION E RROR R ATE (%) Subjects Sub1 Sub2 Sub3 Sub4 Sub5 Sub6 Mean

OF EACH MOTION OF THE AFFECTED SIDE OF THE SUBJECTS ON THE DAY OF ACTIVE T DCS

Sessions

HC

HO

KG

TP

WF

WE

RD

UD

FS

FP

NM

Pre-tDCS Post-tDCS Pre-tDCS Post-tDCS Pre-tDCS Post-tDCS Pre-tDCS Post-tDCS Pre-tDCS Post-tDCS Pre-tDCS Post-tDCS

0.64 0.00 10.82 10.70 68.30 26.16 55.67 50.00 15.34 0.64 44.33 28.35

46.26 23.71 27.71 28.74 20.23 11.34 88.66 15.34 45.23 4.51 49.10 8.63

68.69 0.39 17.01 10.57 56.70 37.63 50.64 62.11 27.96 14.30 47.04 24.61

68.56 32.09 10.31 5.80 60.70 70.10 19.07 35.82 19.33 50.52 39.82 30.67

25.90 13.02 4.90 0.64 16.88 3.61 29.77 23.32 6.70 6.57 14.18 3.87

6.70 0.00 23.20 30.93 52.32 18.69 40.46 37.89 26.68 5.28 1.55 12.24

11.60 59.66 36.73 27.32 48.45 11.73 86.47 35.05 9.02 3.35 0.13 13.66

34.66 12.37 45.62 17.14 39.05 42.91 13.66 18.17 35.05 22.55 17.78 4.64

11.08 0.00 27.84 4.38 11.08 5.41 26.42 16.11 9.02 0.64 21.65 27.06

0.52 2.71 10.82 0.90 49.48 47.29 11.34 17.91 30.28 1.55 2.96 6.31

0.13 0.00 0.90 0.52 0.64 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Pre-tDCS Post-tDCS

32.52 19.31

46.20 15.38

44.67 24.94

36.30 37.50

16.39 8.51

25.15 17.50

32.07 25.13

30.97 19.63

17.85 8.93

17.57 12.78

0.28 0.09

Fig. 4. CER of the intact side of the subjects. The error bar for each subject represents the standard deviation. The rightmost four bars are the average values across all subjects. Error bars for the rightmost four bars represent the standard error.



TABLE III S TABILITY INDEX

OF EACH MOTION OF THE AFFECTED SIDE OF THE SUBJECTS ON THE DAY OF ACTIVE T DCS.

Subjects

HC

HO

KG

TP

WF

WE

RD

UD

FS

FP

Sub1 Sub2 Sub3 Sub4 Sub5 Sub6

0.23 1.71 0.84 1.38 2.69 0.94

0.99 1.64 1.36 1.87 3.43 2.08

3.17 0.56 1.34 0.77 2.40 1.46

1.32 1.85 0.84 1.50 0.92 1.20

1.29 1.46 1.68 1.06 1.49 1.08

1.97 0.99 1.81 0.53 6.19 0.83

0.37 1.07 1.81 1.79 1.64 1.65

0.71 1.44 1.68 1.38 1.33 1.02

0.74 2.30 1.01 6.22 2.37 0.63

0.93 1.47 1.74 1.28 0.82 1.55

Mean

1.30

1.89

1.62

1.27

1.34

2.05

1.39

1.26

2.21

1.30

IV. DISCUSSION The present study investigated whether anodal tDCS of the contralateral primary motor cortex of the affected side could improve the classification performance of the EMG signals acquired from residual muscles in the stump of amputees. The results showed that there was a significant decrease in CER of the eleven phantom motions on the day of active tDCS (p < 0.001), but not on the day of sham tDCS (p = 0.45). After active tDCS, the average CER of the affected side significantly decreased from 27.3% to 17.2% (p < 0.001). A. Improvement of Classification Performance of Affected Side After the first day, enough repetitions and exercise of all the motions stabilized the performance of the affected side as demonstrated by no significant difference between the pretDCS and post-tDCS sessions on the day of sham tDCS (p = 0.45). Therefore, the significant decrease in CER of the affected side on the day of active tDCS should be due to the active anodal tDCS intervention. Because active anodal tDCS could increase the motor cortex excitability [26], it was reasonable to suggest that such a positive change in the cortical activity resulted in more consistent muscle contractions when the subject performed the same motor task, and more distinctive muscle contractions when the subject performed different motor tasks. As a result, the classification performance was significantly improved on the affected side. This intervention rapidly improved the performance of myoelectric control (within 20 minutes), not by advanced algorithms, but by improving the signal conditioning at the source, i.e. the subject’s ability to produce more consistent (same motion) and more distinctive (different motions) EMG signals, as a result of noninvasive cortical stimulation. As shown in Table II, the performance improvements of Sub1, Sub3, Sub5 and Sub6 were mainly from the 4 hand motions (HC HO KG and TP). For Sub2, the improvement was mainly from UD and FS motions. For Sub4, the improvement was mainly from HO and RD motions. From the average CER of each motion across the subjects, we found that the performance improvements for the hand motions were more pronounced than the wrist motions. B. Deterioration of Classification Performance of Intact Side On the intact side, we found no significant difference in CER between the pre-tDCS and post-tDCS sessions during the day of sham tDCS (p = 0.396). Surprisingly, here we

(a)

(b) Fig. 5. Variations in EMG feature space between the pre-tDCS and posttDCS sessions of the affected side on the day of the active and sham tDCS. (a) MSA of the feature vector; (b) SI of the feature vector. Error bars represent the standard error. The rightmost four bars are the average values across all subjects.

found a negative effect on the day of active tDCS, i.e., the average CER of the intact side increased after the anodal tDCS intervention on the day of active tDCS (p = 0.004) (Fig.4). This interesting phenomenon could not be attributed to physical or mental fatigue of the subjects, because the experimental sequence was random. Initially, we thought that tDCS would have no effect on the intact side, because all the subjects only received active anodal tDCS on the motor cortex area related to the affected side, and the cathode was placed



on the forehead instead of the motor cortex area related to the intact side. Previous research only showed that cathodal tDCS could suppress the activity of the cortex [26]. This phenomenon of negative effect on the intact side could be explained by interhemispheric inhibition (IHI), which meant that the activity of the primary motor cortex of one hemisphere inhibited the activity of the other hemisphere [43]. Pal et al. showed that the decrease of excitability in the dominant motor cortex induced a decrease in IHI from the dominant onto the non-dominant motor cortex; consequently, excitability of the non-dominant motor cortex increased [44]. Vines et al. demonstrated that dual-hemisphere tDCS, which placed the cathodal electrode over the dominant motor cortex, improved motor function of the non-dominant hand more than unihemisphere tDCS [45]. They also mentioned that the inhibition effect of cathodal tDCS on the dominant side caused higher excitability of the non-dominant side compared with unihemisphere tDCS. Here, we suggested that the increase of excitability in motor cortex of the affected side induced by anodal tDCS enhanced the IHI from the motor cortex of the affected side onto that of the intact side; therefore, the CER of the intact side increased. However, this mechanism was very complex, and we left this issue as an open question for future work. C. Diversity of Subject Population Table I shows the differences between subject’s amputation and prosthesis usage. Sub3 and Sub4 had their amputations over 30 years and never used myoelectric prostheses. Their CERs of the affected side were much higher than the others, even after the active tDCS intervention (Fig. 3). Taking the experience into account, as Sub1, Sub2 and Sub6 had some experience with myoelectric prostheses, their CERs of the affected side were relatively lower than that of Sub3 and Sub4. A previous study also showed that the CERs of the experienced intact-limb subjects were significantly lower than that of the novice intact-limb subjects [42]. Sub5 had never used myoelectric prostheses before, the same as Sub3 and Sub4, but Sub5 had the shortest time from amputation. So the CER of the affected side of Sub5 was much lower than that of Sub3 and Sub4 in the pre-tDCS session and achieved less than 10% in the post-tDCS session on the day of active tDCS. As illustrated in Fig. 3, the average performance improvement was 8.7% across the three experienced subjects (Sub1, Sub2 and Sub6) and was 11.3% across the three novice subjects (Sub3, Sub4 and Sub5). The average performance improvement across the three experienced subjects was lower than that across the three novice subjects. Since the experienced subjects used the residual muscles’ EMG to control a conventional prosthesis in daily life, the cortex excitability of the affected side of them might be higher than that of the novice subjects. Due to the ceiling effect, the effect of anodal tDCS increasing motor cortical excitability for experienced subjects was relatively smaller than that for novice subjects. From Table I, we knew that three subjects (Sub1, Sub3 and Sub6) were dominant (right) hand amputated and three subjects (Sub2, Sub4 and Sub5) were non-dominant (left) hand

amputated. Boggio et al. demonstrated that, for intact-limb subjects, anodal tDCS enhanced non-dominant hand motor function but could not enhance dominant hand motor function [30]. The results in the present work showed that anodal tDCS significantly improved the classification performance of the affected side for each subject, regardless of the affected side was dominant or non-dominant. There was, apparently, no difference between the dominant and non-dominant sides. A previous functional magnetic resonance imaging (fMRI) study in upper limb amputees demonstrated that the intact hand movements of amputees resulted in a similar activation level to the dominant hand movements in intact-limb controls [35]. We believed that the affected side was used much less than the intact side following amputation, which caused the motor cortical excitability of the affected side to be much lower than that of the intact side. Due to the brain plasticity, the change of cerebral activity might change the innate dominant hand of the unilateral transradial amputees from the dominant side into the intact side. Our results suggested that the motor cortex excitability of the affected side of the amputees was enhanced by anodal tDCS. D. Changes in EMG signals As shown in Table III, active anodal tDCS increased the stability of EMG signals. The results showed that active tDCS induced improvement of the affected side contributed to the increased stability of EMG signals. Since the STs of most motions with greater CER reductions were larger than 1, we attributed the increased stability to the improved ability of the subjects in mastering how to perform these motions after the active anodal tDCS intervention. If the subjects mastered how to perform the motions clearly, as if they had an exact target to reach, the EMG signals of these motions generated by the subjects should be more stable. For motions with greater CER reductions and smaller STs (less than 1), we believed that their CER reductions were mainly induced by the increased interclass distances, even if the EMG signals were not more stable than before. E. Role of tDCS The subjects performed eleven-class hand and wrist motions immediately before and after tDCS. The total time consumed in either the pre-tDCS or the post-tDCS sessions was approximately 60 minutes. Therefore, it was adequate to evaluate the after-effects of tDCS, as a previous study showed that the aftereffects of 13-minute 1 mA tDCS on motor cortex excitability could last 60-90 minutes [46]. We compared the results obtained in this study to those of similar studies on motor function of the non-dominant hand in healthy subjects [30] or motor function of the paretic hand in stroke patients [27], [28]. The magnitude of motor improvement as indexed by the JTT was 9.4% in Baggio’s study, 6.7% in Fregni’s study and 8.9 % in Hummel’s study. All these studies showed that active anodal tDCS, which could increase motor cortical excitability, improved the motor function by nearly 10% for both healthy subjects and stroke patients. Therefore, the average classification performance



improvement (10.1%) of the affected side across the subjects was comparable with these previous studies. For the amputees, their affected side had not been used for an extended period of time, which could decrease the excitability of corresponding motor cortex. Thus, the ability of the amputees to voluntarily access limb movement representations at the affected side was reduced. Because anodal tDCS increased the motor cortical excitability, following the demonstrated intervention based on active anodal tDCS of the motor cortex corresponding to the affected side, the amputees could voluntarily access limb movement representations better. As a result, the amputees could activate consistent (same motion) and distinctive (different motions) EMG patterns, and the myoelectric control performance could be significantly improved. F. Future Work The present work has only investigated the peripheral activities, i.e. EMG signals. It can be inferred that some changes must have happened at the cortical level following tDCS. In the future, we plan to simultaneously collect EMG and brain signals, such as fMRI and electroencephalogram (EEG) signals, to directly evaluate the differences of the motor cortex excitability between the pre-tDCS and post-tDCS sessions. To better understand the changes of muscle activation patterns after anodal tDCS, a high density surface EMG signal acquisition system may be adopted to decompose the signals into individual spike trains of motor unit action potentials, which correspond to firing characteristics of motor neurons [47], [48]. Also, there is a limitation of present study that the subjects are only trained for short time on the first day. Actually, the short-term training cannot accomplish a complete clinical improvement on myoelectric control performance. It is found that the performance will be stable after long-term training [12]. Future work should be conducted to evaluate the effect of anodal tDCS under the condition that the training effect is minimized. There is only one day of active anodal tDCS in present study. Daily tDCS for multiple days is likely necessary to establish a long-lasting improvement of classification performance of EMG signals in the future. As this work is an off-line analysis, an on-line study should be taken into account as well. In the online study, JTT should be adopted to evaluate the effect of tDCS on the motor function of amputee users in control of myoelectric prostheses based on pattern recognition. V. CONCLUSION Neuromodulation, like tDCS, is a novel method to improve the quality of EMG signals for myoelectric prosthesis control. This work showed that active anodal tDCS could significantly enhance EMG classification performance for the affected side of the amputees. Through a short 20-minute active anodal tDCS intervention, the amputees could generate more consistent and distinguishable EMG patterns. The method proposed has huge potential in helping prosthesis users to produce desired EMG patterns quickly and easily, so that the long and difficult user training phase for pattern recognitionbased myoelectric control can be dramatically reduced. This

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Lizhi Pan received the Bachelor’s degree from the School of Power and Mechanical and Engineering at Wuhan University, Wuhan, China, in 2010. He is currently working toward the Master’s and Ph.D. degrees in the School of Mechanical Engineering at Shanghai Jiao Tong University, Shanghai, China. His research interests include signal processing of electromyography and improving myoelectric control.

Dingguo Zhang received the Bachelor’s degree in electrical engineering from Jilin University, China, in 2000, the Master’s degree in control engineering from Harbin Institute of Technology, China, in 2002, and the Ph.D. degree from Nanyang Technological University, Singapore, in 2007. From 2006 to 2007, he was a Research Fellow at Nanyang Technological University. In 2008, he was a Postdoctoral Fellow at LIRMM of CNRS, France. He is currently an Associate Professor at the Institute of Robotics, Shanghai Jiao Tong University, China. His research interests include human-machine interface, rehabilitation technique, biological cybernetics, and biomechatronics. Dr. Zhang is a senior member of IEEE, and a member of EMBS, RAS, and IFESS. He is the winner of Delsys Prize 2011, USA.



Xinjun Sheng received the B.Sc., M.Sc. and Ph.D. degrees in mechanical engineering from Shanghai Jiao Tong University, Shanghai, China, in 2000, 2003 and 2014. In 2012, he was a visiting scientist in Concordia University, Canada. He is currently a lecturer in the School of Mechanical Engineering at Shanghai Jiao Tong University. His current research interests include robotics, and biomechatronics. Dr. Sheng is a member of IEEE, RAS, EMBS, and IES.

Xiangyang Zhu received the B.S. degree from the Department of Automatic Control Engineering, Nanjing Institute of Technology, Nanjing, China, in 1985, the M.Phil. degree in instrumentation engineering and the Ph.D. degree in automatic control engineering, both from Southeast University, Nanjing, China, in 1989 and 1992, respectively. From 1993 to 1994, he was a postdoctoral research fellow with Huazhong University of Science and Technology, Wuhan, China. He joined the Department of Mechanical Engineering as an associate professor, Southeast University, in 1995. Since June 2002, he has been with the School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China, where he is currently a Changjiang Chair Professor and the director of the Robotics Institute. His current research interests include robotic manipulation planning, humanmachine interfacing, and biomechatronics. Dr. Zhu received the National Science Fund for Distinguished Young Scholars in 2005.


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