An improved random retreat DBN recognition method for EEG signals

Abstract:

To solve the problem of long training time and over-fitting of small sample EEG signal processing, this paper proposes a DBN based on random retreat algorithm, which can classify and identify the left and right hand motion imaginary EEG signals. Firstly, the original EEG data were processed by dimension reduction, and the random DBN model was used to train the reduced EEG data, then the optimal parameter values for classification and recognition were obtained. The experimental results show that compared with CSP, PCA and single DBN network, the DBN algorithm based on random retreat can maintain the high recognition rate and reduces the training time, which proves the effectiveness of the method. Finally, the feasibility of the algorithm was verified on the intelligent wheelchair platform.