Classification of mental task EEG records using Hjorth parameters

Abstract

The effects of mental activities on brain dynamics is the main field that studied for a long time, but the results of studies have not reached the desired level. The aim of present study was to classify the mental task EEG records by using Hjorth parameters. In this study, EEG signals that recorded from 9 subjects were used. EEG signals were recorded by applying a experimental paradigm which contains five stimuli related to different mental task. These stimuli are defined as condition word mental subtraction spatial navigation right hand motor imagery and feet motor imagery Wavelet packet transform was used to obtain sub-bands of EEG signals. Statistical parameters that consist of mobility, complexity and Mahalanobis distance were applied to sub-bands. Feature vectors were classified by using artificial neural network. When classification performances related to mental activities were examined, the best classification accuracy was obtained as nearly 80% for `condition word - mental subtraction', (`spatial navigation - feet motor imagery;' and `spatial navigation - condition word'. The lowest classification accuracy was obtained for `mental subtraction - right hand motor imagery,', `condition word - right hand motor imagery' and `spatial navigation - right hand motor imagery'. The classification accuracies related to all stimuli that classifed among themselves were obtained as 77,61%.