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AbstractBrain computer interface (BCI) algorithms areused to predict the torque generation in the direction of

shoulder abduction or elbow flexion using scalp EEG signals

from 163 electrodes. Based on features extracted from both

frequency and time domains, three classifiers are employed

including support vector classifier, classification trees and K

nearest neighbor. Support vector classifier achieves the highest

recognition rate of 92.9% on two able-bodied subjects in

average. The recognition rates we obtained on the able-bodied

subjects are among the highest compared with previous reports

on predicting motor intent using scalp EEG. This demonstrates

the feasibility of separating the shoulder/elbow torques usingscalp EEG as well as the potential of support vector classifier

in applications of BCI. Preliminary experiments on two

hemiparetic stroke subjects using support vector classifier

reports an accuracy of 84.1% in average, which shows an

increased difficulty in predicting intent presumably due to

cortical reorganization resulting from the stroke.

Keywords BCI, EEG, Support Vector Classifier,Shoulder Abduction, Elbow Flexion,

I. INTRODUCTION

Brain computer interface (or BCI) recently emerged as a

technique to convey brain derived signals for

communication and control [1-4]. These studies demonstrate

that BCI provides a non-muscular way to convey a usersintent. In particular, BCI based on electro-encephalogram

(EEG) is a promising candidate for use in human

rehabilitation. Compared with invasive methods that useneuronal action potentials or local field potentials recorded

within the brain, noninvasive EEG signals recorded from

scalp are convenient, safe and inexpensive. Variouscomponents of EEG signals have been found useful for

movement prediction including localized changes in spectral

power of spontaneous EEG related to sensorimotor processes [5][6], slow cortical potentials [7] and various

types of event-related potentials [8-10].However, despite the progress in using BCI for the

prediction of motor intents, the average recognition rate

achieved is around 80% [10-12]. Mensh et al. [13], as the

winner of BCI competition 2003, reported an 88.7%recognition rate for cursor movement in one able-bodied

subject. Whats more, there has been few exploration of

effective BCI algorithms that can separate motor tasksoriginating from close regions on the same hemisphere such

as those correspond to shoulder versus elbow movements.

Further study is thus required before BCI algorithms can be

applied for clinical use especially when brain reorganizationmay occur such as following stroke and spinal cord injury,

which may increase the difficulty of movement prediction.

EEG-based Discrimination of Elbow/Shoulder Torques using Brain Computer

Interface Algorithms: Implications for Rehabilitation

J. Zhou1, J. Yao

2, J. Deng

3, J. Dewald

2,3,4

1Department of Computer Science, Northern Illinois University, DeKalb, IL, USA2Department of Physical Therapy and Human Movement Sciences, Northwestern University, IL, USA

3Department of Biomedical Engineering, Northwestern University, IL, USA4Department of Physical Medicine and Rehabilitation, Northwestern University, IL, USA

In this paper, we present results of a preliminary study

on predicting the subjects intent of shoulder abduction

(SABD) or elbow flexion (EF) using advanced BCI

algorithms based on 163-channel scalp EEG signals. This isthe first attempt using BCI to separate shoulder/elbow motor

tasks in able-bodied and hemiparetic stroke subjects. It isalso the first time such a large number of electrodes are used

in EEG-based BCI study. In order to derive effectivealgorithms, we investigate the performance of combining

time-frequency feature extraction with three differentclassifiers, i.e., Support Vector Classier (SVC),

Classification and Regression Tree (CART), and K-nearestneighbor (KNN). Our preliminary results demonstrate that

SVC has the best potential in predicting upper arm

movements and is a promising candidate for the use of BCI

in rehabilitation applications.

II. METHODOLOGY

A. Experimental Setup

Each subject learned to self-initiate the generation ofisometric shoulder abduction (SABD) or elbow flexion (EF)

at a level of 25% of his/her maximum voluntary torques(MVTs). EEG and torques were collected during the

generation of isometric elbow/shoulder torque.

Subjects were cast at the wrist and secured to a six

degree of freedom (DOF) load cell with the shoulder at 75abduction, 40 flexion and the elbow at a 90 flexion angle.

In this position, the tip of the hand was approximatelyaligned with the median sagittal plane of the subject. In

order to minimize the effect of trunk muscle activation,

subjects were seated in a Biodex chair with the trunk

secured and the shoulders strapped to the back of the chair.A computer monitor was placed in front of the subject to

provide visual feedback of the torque generation in the

training protocol.Scalp recordings were made using a 163-channel EEG

system with active electrodes. The electrodes are mounted

on a stretchable fabric based on a 10/20 system. The capwas fitted on the head of the subject lining the Cz electrode

with the intersection of the planes defined by the nasion,

inion, and pre-auricular points. The skin under eachelectrode site was prepared and conductive gel was injected

Proceedings of the 2005 IEEEEngineering in Medicine and Biology 27th Annual ConferenceShanghai, China, September 1-4, 2005

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to achieve electrode impedances lower than 5 k

throughout the experiment. EEG data were collected at 1000

Hz sampling rate. Anti-aliasing filtering (100 Hz) wasprovided before data acquisition. The system was equipped

active electrodes that provide a first amplification stage,

allowing detection of EEG signals with a higher SNR and

quicker preparation.

B. Feature Extraction

Both frequency and time domain features were

extracted from the EEG signal. First, a finite difference

Surface Laplacian (SL) transformation was applied to eachchannel as a spatial high-pass filter to reduce the smearing

effects caused by head volume conductor and to increase thesignal noise ratio (SNR). Due to the possibility that for the

same motor task subjects use different strategies, we used

principal component analysis (PCA) to calculate the

covariance complexity of the signals. When a data set was

considered highly complex, we divided the set into twosubsets using the average complexity value as the dividingline [14]. Next, we extracted features by decomposing

signals in both frequency bands and time courses. The

constant Q decomposition was conducted in the frequency

domain to divide the signals into frequency bins from 5 to34 Hz with a group of band-pass filters. Q is the ratio of

center frequency over bandwidth (set as 4). The informationin the time domain within each frequency bin is then

obtained by applying Hilbert transformation to extract the

profiles of the oscillatory activities. The profiles were

divided into equal-length (around 55 ms of each interval)with 50% overlapping. The feature for each time interval

was the integrated profile of that time duration, which can

be viewed as the instantaneous power on the correspondingtime-frequency grid. As the result of feature extraction, each

trial can be represented by a spatial pattern over the channel

distribution on the scalp where each channel has its two-dimensional feature (i.e., features in time and frequency

domains).

For more details of the exploited feature extractionalgorithm, refer to [10] and [14]. In this paper, our focus is

on comparing different classifiers, and particularly

examining the performance of support vector classifier inseparating the shoulder versus elbow movements in able-

bodied and hemiparetic stroke subjects.

C. Classifiers

a. Support Vector Classifier

The support vector classifier is a relatively new

classification technique developed by Vapnik [15] which

has reported strong performance in a number of real-worldapplications including BCI [12][16]. The basic idea is to

find an optimal separating hyperplane for two classes thatmaximizes the margin (see definition of margin below). The

algorithm looks for the optimization weight vector (w) and

offset (b) of the discriminating hyperplane by solving the

following quadratic problem:

minw,b

{|| w ||2 C i2

i

I

}

subject to,1)( iii bxwy

i

iiii Iixyw .,..,1,;0

where i are called slack variables which ensures that the problem has a solution when the data are not linearly

separable; yi is the ith label; i is the index of the sample

(vector); and i is the Lagrange coefficient obtained whenusing Lagrange theory to solve the above optimization

problem. The margin is defined as 2/||w||2. Geometrically, as

illustrated by Figure 1, the margin is the distance between

the bounding planes of the two data sets. The data vectorson the bounding planes are critical for finding the solution.

They are called support vectors, which explains the name ofthe algorithm.

To solve nonlinear problems, support vector classifier

conducts a transformation from input data space to featurespace by applying a kernel function to the data. Most

commonly used kernel functions are polynomial and

Gaussian. For details, see [17].

Fig. 1 Hyperplane and margin in support vector classifier.

b. Classification and Regression Tree

Classification and regression tree (CART) [18] is a

multi-stage binary decision tree. Every leaf node isassociated with a category. Every other node contains a

decision rule which partitions the feature space into two.The training process of CART typically contains the

stage of constructing a tree and pruning the tree to reduce

model complexity and avoid overfitting. The testing processwith CART is the process of following the decision rules of

the branches until a leaf node is reached and the category

associated with the leaf node determines the class label forthe testing sample.

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As a classifier, CART has the advantage of doingimplicit feature extraction during the process of tree

construction. In other words, only the features are relevantwith the decision making are contained in the tree. However,

the decision boundaries of CART are linear and typically

parallel to the coordinates, therefore, its performance under

some conditions is less optimal.

c. K Nearest Neighbor

K nearest neighbor (KNN) is a simple classifier that

assigns a label same as the majority label among K nearest

neighbors of the testing sample. The distance measure between samples is application-specific and we used the

correlation coefficient between two feature maps of twotrials.

III. R ESULTS

A. Results from Able-bodied Subjects

Experiments were conducted on two able-bodied male

subjects both with right hand dominance. Each subject

performed around 100 trials of torque generation in eitherSABD or EF directions.

The evaluation of the algorithms consisted of two parts:training and testing. The training procedure provided each

time-frequency interval a weight w(t,f) [0, 1] proportionalto its contribution. It was calculated using the trainingrecognition rate achieved by each classifier on the particular

frequency/time grid. Following training, a testing procedure

was performed using 17 fold cross-validation. We dividedthe data into 17 sets with about equal numbers of trials per

set for each movement type. In each fold, one set was used

for testing and all the other trials were used to train theclassifier. The reported result is an average on 17 folds.

LibSVM 2.6 [19] is used in our experiment with default

parameters. For each fold, we calculated the recognition rateas Nc/N, where N is the number of total test trials and Nc is

the number of correctly predicted test trials.

The recognition rates for the two subjects are listed in

Table 1. Figure 2 depicts the weight distribution in thefrequency and time domains for the two subjects.

TABLE 1RECOGNITION RATE OF THREE BCI ALGORITHMS ON TWO SUBJECTS TO

DISCRIMINATE SHOULDER ABDUCTION AND ELBOW FLEXION.

Subjects\methods Nearest

Neighbor

Classifier

Classification

and

Regression

Tree

Support

Vector

Machine

N1 89.1% 87.6% 94.7%

N2 90.7% 82.1% 91.0%

Mean 89.9% 84.9% 92.9%

Figure 2 Weights on the frequency and time domain.

Our analysis resulting in the following observations::

1 The support vector classifier outperforms the other two

methods of CART and K Nearest Neighbor. Extracting

features from time and frequency domains makes it an

effective BCI algorithm allowing for an effective

separation between the intent to generate shoulder

abduction versus elbow flexion torques using scalp

EEG signals.

2 The reported recognition rate of 92.9% is among the

highest BCI performances among reports of EEG-based

motor classifications.

3 Figure 2 suggests that some frequency bands have

higher weights than others. However, the physiological

meaning of these differences in weights still requires

further exploration.

4 This paper reports one of the few experiments that uses

as many as 163 electrodes in EEG-based BCI. The

result confirms the feasibility and effectiveness of using

a large number of electrodes for signal collection. In

future work, we will seek to determine the optimalnumber of electrodes required to obtain the best

recognition rates.

B. Results from Hemiparetic Stroke Subjects

Obligatory coupling between certain shoulder andelbow torques is typically found in the moderately to

severely impaired arm stroke patients and results indiscoordination during movement. If movement intentions

can be successfully separated using BCI, then in the long

run, we may apply the algorithms to drive rehabilitation

interventions for the treatment of upper limb

discoordination. However, the associated challenge is theexpected brain reorganization following stroke [20], whichcan make the separation between shoulder and elbow

movement intentions more difficult.

To investigate the effectiveness of our algorithms and

the impact of an increased overlap in sensorimotor corticalareas used during elbow versus shoulder tasks following

stroke, experiments were conducted on two hemipareticstroke subjects.. Features were extracted from EEG signals

as described in Section II(B). Torques were separated using

the support vector classifier. Information regarding the two

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subjects and preliminary results are listed in Table 2. Theerror rate is calculated as Ne/N, the recognition rate is

defined as Nc/N and the accuracy is defined as Nc/(N-Nr),whereNis the number of total test trials,Nc is the number of

correctly predicted test trials,Ne is the number of incorrectly

predicted test trials, andNris the number of rejected trials.

TABLE 2INFORMATION OF STROKE SUBJECTS AND DISCRIMINATION RESULTS

FOR SHOULDER ABDUCTION AND ELBOW FLEXION.SMA = Supplementary Motor Area; PM = Primary Motor. The results on eachsubject are averages of 17-fold cross validation.

Results\Subjects S1 S2 Mean

Age 60 51 -

Sex Male Female -

Dominant Hand Left Right -

Affected Hand Left Right -

Lesion Position R.

posterior

limb of IC

L. dorsal

lateral SMA

& PM

subcortical

white matter

-

Error Rate 12.5% 10.3% 11.4%

Recognition

Rate

48.0% 54.2% 51.1%

Accuracy 82.8% 85.3% 84.1%

As shown by Table 2, on one hand, the results show that

the reduced spatial resolution at sensorimotor cortices

increased the difficulty of prediction since the average errorrate (11.4%) is higher than that of able-bodied subjects

(7.1%); on the other hand, with the added rejection scheme,we achieve an average of 84.1% prediction accuracy on

impaired subjects. This demonstrates the feasibility of our

BCI algorithm in stroke patients and represents promising

preliminary results considering the challenge caused bybrain reorganization after stroke.

IV. CONCLUSION

In summary, this work demonstrates that support vector

classifier, together with frequency/time domain featureextraction, are effective algorithms for separating shoulder

abduction and elbow flexion intents, and thus suggests a

promising direction for EEG-based discrimination for otherupper limb movements as well as future applications in the

field of neuro-rehabilitation.

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