An effective approach of eeg signal based human emotion recognition using advanced classification techniques

Abstract

newline The work describes about the EEG and brain wave pattern analysis are related to the specific activity of the human being. Mainly EEG signal is the process of analyzing the Electrical potential of the human activities for the every changing. The brain is the most complex organ in the biological system. Recent studies that clearly describes the EEG data signal provides a clear classification accuracy of the human activities. The obtained EEG signal has different brain wave pattern daily activities like sleeping, reading and watching a movie. These are activities will provide different emotion in our brain so, we collect that different types of emotion signals in brain like Delta, Theta and Alpha bands. However, due to the non stationary behavior of EEG recordings, time frequency domain methods typically lead to higher successes. Moreover, it is known that the time frequency representation ability to analyze different neural rhythm scales can be used as a reliable EEG marker; this ability has been shown to be a powerful tool for investigating small scale neural brain oscillations. Accordingly, close relationships are often established between Frequency and signal variation because they highly vary with changes in an activity state. Particularly, the and#948; and and#945; rhythms that exhibit higher frequencies and lower magnitudes with respect to and#952; waves may examines the activities of the human being. Consequently, a quantitative contribution of each frequency sub band must be clearly expressed toward automatic identification and monitoring of specific activities. In order to measure the contribution of time variant Frequency to the representation of brain activity, the following stage must be carried out: i) the estimation of physiological rhythms highlighting the non stationary behavior of EEG data. newline newline