EEG Emotion Recognition Based on Deep Compressed Sensing

Abstract

Purposes Deep compressed sensing is the use of deep learning to solve the problems existing in traditional compressed sensing, such as the adaptability of observation matrix to traditional signal compression and the dependency on dictionary by reconstruction algorithm. Methods In this paper, the deep belief network (DBN) is used to adaptively compress the signal without destroying the randomness of observation matrix. At the same time, the stacked auto encoder (SAE) is used to train the reconstruction network end-to-end to get rid of the dependence of the reconstruction algorithm on sparse dictionary. According to the discrimination of the sparse representation of signal, a compressed sensing recognition model based on DBN and SAE is proposed (CS-DBN-SAE). Findings The results of four classification experiments on DEAP emotional EEG database show that the recognition rate of CS-DBN-SAE model is 83.29%, which is oven 4.3% higher than that of traditional compressed sensing recognition model.

Keywords

• compressed sensing
• deep belief network
• stacked auto encoder
• eeg signal
• emotion recognition