A Zero-Padding Frequency Domain Convolutional Neural Network for SSVEP Classification

Frontiers in Human Neuroscience

Yi Xiao¹  Yongqing Zhang²  Wenyin Zheng³  Lutao Wang³  Manqing Wang⁴  Dongrui Gao⁴ 

[1] National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, China;School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China;School of Computer Science, Chengdu University of Information Technology, Chengdu, China;School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China;

关键词: electroencephalogram;    zero-padding frequency domain;    steady-state visual evoked potential;    steady-state motor visual evoked potential;    convolutional neural network;   

DOI  :  10.3389/fnhum.2022.815163

来源: DOAJ

【 摘 要 】

The brain-computer interface (BCI) of steady-state visual evoked potential (SSVEP) is one of the fundamental ways of human-computer communication. The main challenge is that there may be a nonlinear relationship between different SSVEP in other states. For improving the performance of SSVEP BCI, a novel CNN algorithm model is proposed in this study. Based on the discrete Fourier transform to calculate the signal's power spectral density (PSD), we perform zero-padding in the signal's time domain to improve its performance on the PSD and make it more refined. In this way, the frequency point interval in the PSD of the SSVEP is consistent with the minimum gap between the stimulation frequency. Combining the nonlinear transformation capabilities of CNN in deep learning, a zero-padding frequency domain convolutional neural network (ZPFDCNN) model is proposed. Extensive experiments based on the SSVEP dataset validate the effectiveness of our method. The study verifies that the proposed ZPFDCNN method can improve the effectiveness of the SSVEP-based high-speed BCI ITR. It has massive potential in the application of BCI.

【 授权许可】

Unknown