A Comparison of EMG and EEG Signals for Prostheses Control using Decision Tree

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1 Technology Volume, Issue, July-September, 3, pp. -8, IASTER 3 Online: , Print: A Comparison of EMG and EEG Signals for Prostheses Control using Decision Tree V. V. Ramalingam, S. Mohan, 3 V. Sugumaran Research Scholar, Bharathiar University, Coimbatore, India Professor & Dean - Department of CSE, Sri Eshwar College of Engineering, Coimbatore, India 3 Associate Professor - SMBS, VIT University, Chennai Campus, Chennai, India, ABSTRACT In spite of availability of various approaches, the control of prosthetic limb would be more effective if it is based on Electromyogram (EMG) signals from remnant muscles and Electroencephalogram (EEG). The analysis of these signals depends on various factors such as amplitude, time and frequency domain properties. EEG signals are obtained from the eperiments conducted in Biomedical laboratory using 7 different subjects with four different hand movements viz., finger open (fopen), finger close (fclose), clock wise wrist rotation (cw) and counter clock wise wrist rotation (ccw). The EMG dataset for the same conditions were obtained from the NINAPRO DATABASE, a resource for bio robotics community of hand movements. The statistical features were etracted for both EMG and EEG signals and classified using decision tree (C4.5) algorithm. The comparison of classification accuracies for both EMG and EEG signals is presented. Keywords: Classification, Decision tree, Electromyogram (EMG) signals, Electroencephalogram (EEG) signals, Statistical feature.. INTORDUCTION The main requirement of prosthetic arm is that it should be as near as possible to a natural arm. The artificial arm can either be mechanical, electrical or myoelectric. Myoelectric control is based on the electromyogram (EMG), which is a measure of neuromuscular activity detected directly from the muscle or from the skin surface. A myoelectric control system maps a set of features drawn from the myoelectric signal to a particular function, such as fleion of a prosthetic wrist. In this type of system, a user with the potential for naturally-evoked movement control []. There are some difficulties in etracting sufficient information from the EMG for prosthetic control like electrode placement, electrode type, skin and the muscle. The Autoregressive model will overcome the electrode placement noise []. The EEG signal is highly comple; it is one of the most common sources of information used to study brain function and neurological disorders (Agarwal, Gotman, Flanagan, & Rosenblatt, 998; Adeli, Zhou, & Dadmehr, 3; Hazarika, Chen, Tsoi, & Sergejew, 997). In general, interpretation of EEG signals is very comple and thus it demands lot of epertise. Computers have been proposed to solve this problem and thus, automated systems to recognize electroencephalographic changes. The long-term EEG recordings for proper evaluation and treatment of neurological diseases and prevention of the possibility of the analyst missing (or misreading) information (Agarwal et al., 998; Adeli et al., 3; Hazarika et al., 997). Hence, it is very clear

2 International Journal of Research in Computer Applications & Information Technology, Volume-, Issue-, July-September, 3, (O) (P) that there are only few literature available to guide for selecting the suitable signals for the identification of hand movements. An attempt has been made with EMG and EEG signals with statistical features decision tree based classification for the above problem. The paper is organized as follows, the section, deals with system architecture and data acquisition. In section 3, Feature etraction concepts. In section 4, brief description of classifier (Decision tree) is given. In section 5, result and discussion of application of the decision tree trained by EMG and EEG signals are presented. Finally, in section 6 the drawn conclusions are emphasized.. SYSTEM ARCHITECHTURE AND DATA ACQUISITION The EMG and EEG signals were acquired after proper skin preparations and amplified before being filtered and sampled. The preprocessed signals were used to etract the features and fed into the classifier as shown in Fig.. EMG and EEG ACQUISITION Artifacts Removal Data Filtering Feature Etraction Classification. Data Acquisition Fig.. System Architecture of prosthetic arm The Electroencephalogram (EEG) eperimental setup primarily focus on data from 7 healthy subjects while performing four different classes viz., fopen, fclose, cw and ccw. The EEG signals were collected from the subject s four different hand movements. The data in the EEG database were obtained by the following procedure: The subject was made to sit on an adjustable chair, instructed to have electrodes (C3, C4, CZ, FZ and PZ) with conductive gel medium on scalp surface. Initial signal artifacts due to head motion will be generally ignored in the analysis. Eperiment will be scheduled based on specific time series with respect to the classes. Signals from neurons are acquired with the help of five electrodes which in turn connected with Electroencephalogram device with the frequency ranges from 8 to 3 Hz. In order to avoid muscle fatigue and its influence on the EEG signal, minutes of rest are allowed between the training sequences Fig.a Time domain EEG Signals -5 5

3 International Journal of Research in Computer Applications & Information Technology, Volume-, Issue-, July-September, 3, (O) (P) Fig.b Time domain EMG signals 5 NINAPRO database consists of EMG data from the upper limbs of 7 subjects while performing 5 finger, hand and wrist movements. The database is publicly available to download in standard ASCII format [3]. The EMG was collected from a subject s forearm skin while performing a number of movements of interest, or producing force patterns of interest. Out of 5 movements, in the study only four finger movements fopen, fclose, cw and ccw were considered. The data in the NINAPRO database was acquired by the following procedure: The subject sat comfortably on an adjustable chair, in front of a table with a large screen. The EMG electrodes, data glove and inclinometer were worn on the right hand. The subjects were presented with short movies appearing on the screen and were asked to simply replicate the movements depicted in the movies as accurately as possible. After the training phase, a sequential series of ten repetitions of each class of movements was presented to the subject while data are recorded. Each movie lasts five seconds and three seconds of rest are allowed inbetween movements. In order to avoid muscle fatigue and its influence on the EMG signal, 5 minutes of rest was allowed between the training sequences. 3. FEATURE EXTRACTION From the EMG and EEG signals, descriptive statistical parameters such as mean, median, mode, kurtosis, skewness, standard error, standard deviation, minimum, maimum, sum, and range were computed to serve as features. They are named as statistical features here. Brief descriptions about the etracted features are given below. (a) Standard error: Standard error is a measure of the amount of error in the prediction of y for an individual in the regression, where and y are the sample means and n is the sample size. y y Standard error of the predicted, Y y y n (b) Standard deviation: This is a measure of the effective energy or power content of the EEG and EMG signal. The following formula was used for computation of standard deviation. 3

4 Technology, Volume-, Issue-, July-September, 3, (O) (P) Standard Deviation nn ( ) (c) Sample variance: It is variance of the signal points and the following formula was used for computation of sample variance. Sample Variance nn ( ) (d) Kurtosis: Kurtosis indicates the flatness or the spikiness of the signal. Its value is very low for normal condition of the EEG and EMG signal and high for faulty condition of the EEG and EMG due to the spiky nature of the signal. Kurtosis 4 n( n ) i 3( n ) ( n )( n )( n 3) s ( n )( n 3) where s is the sample standard deviation. (e) Skewness: Skewness characterizes the degree of asymmetry of a distribution around its mean. The following formula was used for computation of skewness. Skewness n n i s 3 (f) Range: It refers to the difference in maimum and minimum signal point values for a given signal. (g) Minimum value: It refers to the minimum signal point value in a given EEG and EMG signal. Therefore, it can be used to detect faulty EEG and EMG signal condition. (h) Maimum value: It refers to the maimum signal point value in a given signal. (i) Sum: It is the sum of all feature values for each sample. 4. DECISION TREE (C4.5) A decision tree is a tree based knowledge methodology used to represent classification rules [4-6]. A standard tree induced with C4.5 consists of a number of branches, one root, a number of nodes and a number of leaves. One branch is a chain of nodes from root to a leaf; and each node involves one attribute. The occurrence of an attribute in a tree provides the information about the importance of the associated attribute. The procedure of forming the Decision Tree and eploiting the same for feature selection is eplained by Sugumaran. 4.. Information Gain and Entropy Reduction Information gain is the epected reduction in entropy caused by portioning the samples according to this feature. Entropy is a measure of homogeneity of the set of eamples. Information gain measures how well a given attribute separates the training eamples according to their target classification. The measure is used to select among the candidate features at each step while growing the tree. 4

5 Technology, Volume-, Issue-, July-September, 3, (O) (P) Information gain (S, A) of a feature A relative to a collection of eamples S, is defined as: Gain ( S, A) where, Entropy ( S) v Value( A) Sv Entropy ( Sv ) S Values (A) is the set of all possible values for attribute A, S v is the subset of S for which feature A has value v (i.e., S v = {s S A(s) = v}). Note the first term in the equation for Gain is just the entropy of the original collection S and the second term is the epected value of the entropy after S is partitioned using feature A. The epected entropy described by the second term is the direct sum of the entropies of each subset S v, weighed by the fraction of samples S v / S that belong to S v. Gain (S,A) is therefore the epected reduction in entropy caused by knowing the value of feature A. c Entropy( S) P i log P i Entropy is given by i Where, c is the number of classes and p i is the proportion of S belonging to class i. Fig. 3a. Decision tree of EEG signals Fig. 3b. Decision tree of EMG signals 5

6 Technology, Volume-, Issue-, July-September, 3, (O) (P) RESULT AND DISCUSSION The data sets described in section. were taken and statistical parameters namely mean, median, standard deviation, variance, skewness, kurtosis etc., were computed for each signal for EMG and EEG signals as eplained in section 3. Then C4.5 algorithm was used to perform the dimensionality reduction. A decision tree was developed as shown in Fig. 3a and Fig. 3b. and therefrom the good features that contribute well for the classification were selected following footsteps of Sugumaran [5]. For EEG signals, out of 6 features (5 channels, each statistical features), the selected statistical features are C3mean, C3standard error, C4maimum, CZmedian, CZrange, FZstandard error, FZkurtosis, and FZminimum. Similarly, for EMG signals, out of 96 features (8 channels, each statistical features), the selected statistical features are C5kurtosis, C5minimum, and C6skewness. With these features the classification accuracy was computed using C4.5 decision tree algorithm. The minimum number of objects required to form a class (M) was varied from to 7(total number of signals per class) and the corresponding classification accuracies were noted down. The value of M which gives maimum classification accuracy was fied and confidence factor was varied from to in steps of.. The variation of classification accuracy with respect to confidence factor and minimum number of objects is shown Fig. 4. Top two graphs shows for EEG signals and bottom two graphs are for EMG signals. The best classification accuracy of 4.74% for EEG signals was achieved with M value of 8 and confidence factor of.. While EMG signals could produce a best classification accuracy of 38.88% with M value of 8 and confidence factor of.. Fig. 4. Training of Classifiers 6

7 Technology, Volume-, Issue-, July-September, 3, (O) (P) The misclassification information is best epressed with the help of confusion matri. For the present study, with best parameters the developed confusion matrices are shown in Fig.5. The interpretation of the confusion matri is as follows: The diagonal elements in the confusion matri (Refer Fig.5) show the number of correctly classified instances. In the first row, the first element shows number of data points that belong to fopen class and classified by the classifier as fopen. In the first row, the second element shows the number of data points belonging to fopen class but misclassified as fclose. In the first row, the third element shows the number of fopen data points misclassified as cw. In the first row, the fourth element shows the number of fopen data points misclassified as ccw and so on. Fig.5 Confusion matri of decision tree 6. CONCLUSION The prosthetic arm using decision tree based approach is a possibility. In the present study an attempt is made to compare the performances of EMG and EEG signals. For this purpose descriptive statistical parameters were used as features and decision tree was used as a classifier. The EEG signals give a classification accuracy of 4.74% while the EMG signals give a classification accuracy of 38.88%. From the above result and discussion one can conclude that EEG signals is better suited for prosthetic arm. The signal processing of EEG and EMG signals is a comple task and requires sophisticated techniques to yield better classification accuracy. 7

8 Technology, Volume-, Issue-, July-September, 3, (O) (P) ACKNOWLEDGEMENTS The EEG signals were eperimentally obtained from Bio-medical Department in S.R.M University, Kattankulathur, Chennai and EMG dataset collected from NINAPRO DATABASE A RESOURCE FOR BIOROBOTICS COMMUNITY. REFERENCES [] Angkoon Phinyomark, Chusak Limsakul and Pornchai Phukpattaranont, A Novel EMG Feature Etraction for Tolerance of Interference, Journal of 3th Annual Sysposium on Computational Science and Engineering, pp.47-43, 9. [] P.C Doerschuk, D.E. Gustafson and A.S. Willsky, Upper Etremly Limb Discrimination Using EMG Signal Analysis. IEEE Transactions on Biomedical Enigineerinig, Vol. 3, No. PP.8-9, 983. [3] Manfredo Atzori, el. Al., Building the Ninapro Database: A Resource for the Bio robotics Community. IEEE International Conference on Biomedical Robotics and Biomechatronics, pp ,. [4] J. R. Quinlan. Induction of Decision Trees, Machine Learning, volume, pp. 8-6, 986. [5] V. Sugumaran, V. Muralidharan, K.I. Ramachandran, Feature selection using Decision Tree and classification through Proimal Support Vector Machine for fault diagnostics of roller bearing, Mechanical Systems and Signal Processing, Volume, Issue, February 7, Pages [6] N. R. Sakthivel, V. Sugumaran, Binoy. B. Nair, Comparison of decision tree-fuzzy and rough set-fuzzy methods for fault categorization of mono-block centrifugal pump, Mechanical Systems and Signal Processing, Volume 4, Issue 6, August, Pages