EEG efficient classification of imagined hand movement using RBF kernel SVM

Abstract

Brain-machine interface (BMI) is a system that allows a person to control a device such as a robot arm using only his or her brain activity. This work is aimed at discriminating between left and right imagined hand movements using a Support Vector Machine (SVM) classifier. The main focus here is to search for the best features that efficiently describe the electroencephalogram (EEG) data during such imagined gestures. The EEG dataset used in this research was recorded using channels F3 and F4 from the Emotiv EPOC neural headset. Feature extraction was performed by processing the EEG data using two methods namely the continuous Wavelet Transform (CWT) combined with the Principal Component Analysis (PCA). The features were fed through a Linear and RBF Kernel SVM classifier. The Experimental results showed high performance achieving an average accuracy across all the subjects of 92.75% with a RBF kernel SVM classifier compared to 81.12% accuracy obtained with Linear SVM classifier.