Spatial Temporal based Classification for Antebrachium and Carpus Movement of EEG Data Using Emotive Head set

Abstract

Electroencephalographic (EEG) signals from test subjects are used for BCI analysis and classification of the four upper limb movements. Brain computer interface (BCI) system provides better neural control to the user, over two antebrachium and two carpus movement. This paper uses Epoc Emotiv head set with fourteen electrodes to acquire signal from the motor area of scalp. A particular protocol for data acquisition is followed. Our method is based on the time domain feature extraction techniques. Activity feature is applied on the signals. In order to discriminate between four upper limb movements within the data set, the paper uses Quadratic support vector machine, RBF SVM and Multilayer perceptron. The best classification accuracy of two antebrachium and two carpus movement is achieved by Quadratic SVM and RBF SVM when Gaussian window is used to calculate the activity feature vector. The number of correctly classified instants in term of percentage is 76.92% and 75.96% for quadratic SVM and RBF SVM. Current classifiers show promising results, where as to make conclusion more generalized and to enhance the classification accuracy, further experiments need to be carried out.