Wavelet based analysis of EEG signal for evaluating mental behavior

The purpose of the research is to evaluate the different human mental behavior through Electroencephalogram (EEG) signal with time-frequency analysis by receiving information from the internal changes of brain state. The paper presents the detection of human mental states based on some salient features of EEG signal. For this purpose seven emotional states have been specified such as relax, thought, memory related, motor action, pleasant, fear, and enjoying music. Several EEG signals have been collected for these states and analyzed using discrete wavelet transform. The discrete wavelet transform (DWT) is used to extract different significant features from the analyzed signal by computing the subband coefficients and applying statistical measures on them. Among various statistical measures maximum and minimum value, mean and standard deviation of wavelet coefficients in each subband are chosen which indicate the dispersion in different mental states and help to evaluate them. The analyzed results are compared with the spectrum analysis. It is found that wavelet analysis provides more effective way in the functioning of the brain to study of mental behavior in compare with Fourier analysis.