A Method of Extracting and Identifying College Students’ Music Psychological Features Based on EEG Signals

With the development of information technology, music education in universities is also changing. Traditional can not effectively explore feature students, resulting quality being restricted. The rapid Electroencephalogram (EEG) signals has brought a new educational model to education. Through extraction students’ psychological features by EEG, be identified and different programs formulated according results. Multifeature combination method improve accuracy EEG extraction. Using empirical mode decomposition wavelet packet two kinds methods analyze data, respectively, then average energy, volatility index, sample entropy, approximate entropy multiscale such as permutation Hurst select combination, classify set after so find out performance optimal combination. experimental results show that better represent main characteristic decomposition, recognition more than 90%.