Mental task classification using wavelet transform and support vector machine

Abstract

The present research is about the various mental tasks, experienced by humans with cognitive function disorders, classified using discrete wavelet transform (DWT) and support vector machine (SVM). The electroencephalogram (EEG) database was obtained from online brain-computer interface (BCI) competition paradigm III and offline B-alert EEG machine was from CARE Hospital, Nagpur. EEG signals from paralysed patients decomposed into the frequency sub-bands using DWT and a set of statistical features extracted from the sub-bands represent the distribution of wavelet coefficients used to reduce the dimension of data, features applied to SVM for classification of left hand and right hand movement. With this system, classification of EEG signals was done with accuracy 91.66% for BCI competition paradigm III and 97% for B-alert machine.