DSCNN: Dilated Shuffle CNN Model for SSVEP Signal Classification

Abstract

The steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) system has attracted a lot of attention. It is a great challenge to increase the classification accuracy of SSVEP, especially in fatigue state. In this paper, we propose a dilated shuffle convolutional neural network (DSCNN) model to realize EEG-based SSVEP signal classification. Firstly, we conduct experiments to obtain SSVEP recordings in normal and fatigue states. Then combining continuous wavelet transform (CWT) and DSCNN, we construct a framework for realizing the SSVEP detection. In DSCNN, the signals are processed by three parallel dilated convolution layers firstly, then we extract the characteristics of the signals through channel shuffle and group convolution, while reducing the computational load. For normal condition, we reach an average accuracy rate of 96.75%, and for the data under fatigue state, the average accuracy of this method increases to 77.52%. Through the comparison with the existing methods, the effectiveness and advance of our method are proved, and the effect of channel shuffle on signal extraction is also demonstrated by comparison.