Relative wavelet bispectrum feature for alcoholic EEG signal classification using artificial neural network

This paper proposes a novel relative wavelet bispectrum (RWB) approach for EEG signal feature extraction method to differentiate the signal between the alcoholic over the non-alcoholic subjects. Firstly, the EEG signal is calculated for its autocorrelation frequencies as the basic step in the bispectrum calculation. Then, the discrete wavelet transform (DWT) is applied substituting the FFT which usually is used in the bispectrum calculation. Lastly, the relative value of each frequency band is calculated for both the approximation and the details parts, producing the RWB. The proposed methodology is implemented in an alcoholic automated detection system using 1200 data samples from UCI EEG Database for alcoholism. Based on the experiments, the setting value of lag in the autocorrelation calculation was evidently very influential on the recognition rate obtained, i.e. the maximum value for the lag was the best. Using cross validation, the highest results from RWB feature extraction method with ANN classifier achieved about 90% recognition rate.