Comparative Study on EEG Feature Recognition based on Deep Belief Network

Abstract

In Brain Computer interface (BCI) system, motor imagination has some problems, such as difficulty in extracting EEG signal features, low accuracy of classification and recognition, long training time and gradient saturation in feature classification based on traditional deep neural network, etc. In this paper, a deep belief network (DBN) model is proposed. Fast Fourier transform (FFT) and wavelet transform (WT) combined with deep machine learning model DBN were used to extract the feature vectors of time-frequency signals of different leads, superposition and average them, and then perform classification experiments. The number of DBN network layers and the number of neurons in each layer were determined by iteration. Through the reverse fine-tuning, the optimal weight coefficient W and the paranoid term B are determined layer by layer, and the training and optimization problems of deep neural networks are solved. In this paper, a motion imagination and Motion observation (MI-AO) experiment is designed, which can be obtained by comparing with the public dataset BCI Competition IV 2a. The DBN model is used to compare with other algorithms, and the average accuracy of binary classification is 83.81%, and the average accuracy of four classification is 80.77%.