title: Comparative study of PCA in classification of multichannel EMG signals.
abstract: Electromyographic (EMG) signals are abundantly used in the field of rehabilitation engineering in controlling the prosthetic device and significantly essential to find fast and accurate EMG pattern recognition system, to avoid intrusive delay. The main objective of this paper is to study the influence of Principal component analysis (PCA), a transformation technique, in pattern recognition of six hand movements using four channel surface EMG signals from ten healthy subjects. For this reason, time domain (TD) statistical as well as auto regression (AR) coefficients are extracted from the four channel EMG signals. The extracted statistical features as well as AR coefficients are transformed using PCA to 25, 50 and 75 % of corresponding original feature vector space. The classification accuracy of PCA transformed and non-PCA transformed TD statistical features as well as AR coefficients are studied with simple logistic regression (SLR), decision tree (DT) with J48 algorithm, logistic model tree (LMT), k nearest neighbor (kNN) and neural network (NN) classifiers in the identification of six different movements. The Kruskal-Wallis (KW) statistical test shows that there is a significant reduction (P < 0.05) in classification accuracy with PCA transformed features compared to non-PCA transformed features. SLR with non-PCA transformed time domain (TD) statistical features performs better in accuracy and computational power compared to other features considered in this study. In addition, the motion control of three drives for six movements of the hand is implemented with SLR using TD statistical features in off-line with TMSLF2407 digital signal controller (DSC).
authors: School of Electrical Engineering, VIT University, Vellore, 632 014, TN, India, pganjali78@hotmail.com.
authors: P
authors: P
authors: Geethanjali
mesh_terms: D000465:Algorithms; D003663:Decision Trees; D004566:Electrodes; D004576:Electromyography / Q000145:classification*; D006801:Humans; D016015:Logistic Models; D008297:Male; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted