Disclosed is a convolution neural network analysis method based on wavelet transform for motion imagination brain signal recognition. The convolution neural network analysis technique based on wavelet transform for motion imagination brain signal recognition uses a brain wave analysis program of a computer connected to an electroencephalogram (EEG) measuring device connected to multiple EEG detection sensors to be worn on right and left heads. Brain wave measurement is performed during left-hand motion imagination and right-hand motion imagination by a brain wave measurement, amplification, A/D conversion, and wavelet transform computer interface (brain wave analysis program). Among EEG data signals measured by band pass filters (BPF2, BPF3) of the EEG measuring device, only mu (8 to 13 Hz alpha wave) and beta (13 to 30 Hz beta wave) band frequency domains are extracted from the output spectrum along the frequency axis. The motion image EEG signal is wavelet-transformed to extract and classify the characteristics of a motion imagination brain signal based on a one-dimensional convolution neural network (CNN) for a time-frequency image.
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