Steady-state visually evoked magnetic signal classification and BCI evaluation based on a convolutional neural network

doi:10.1109/ACCESS.2024.3524397

	Steady-state visually evoked magnetic signal classification and BCI evaluation based on a convolutional neural network
	Yutong Wei 1; Chaofeng Ye2
	2024
发表期刊	IEEE ACCESS (IF:3.4[JCR-2023],3.7[5-Year])
ISSN	2169-3536
卷号	PP 期号:99
发表状态	已发表
DOI	10.1109/ACCESS.2024.3524397
摘要	The steady-state visually evoked magnetic field (SSVEF) is a promising modality in brain-computer interference (BCI), which has the advantages of being non-invasive and non-contact. The combination of optically pumped magnetometers (OPM) and artificial intelligence technology makes SSVEF measurements more portable, accurate, and cost-effective. This paper examines the distribution of the human brain visually evoked magnetic field experimentally and then presents an SSVEF measurement system based on an OPM. A three-block temporal convolutional neural network (3B-TCN) is developed to classify brain magnetic signals. A data augmentation method based on statistical analysis of SSVEF signals is proposed, which generates 30,000 sets of data to train the 3B-TCN. The SSVEF signal classification accuracies of the 3B-TCN network are 96.61%, 92.36%, and 86.75% for 10 s, 5 s, and 2 s time length data, respectively. The impact of visually fatigued states on BCI is studied. The accuracy of controlling the character in the game is above 90% when the subjects are in a normal state, but it decreases considerably when the subjects are visually fatigued. The experimental results demonstrate the feasibility of realizing BCI using an OPM sensor and a convolutional neural network.
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来源库	IEEE
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/467854
专题	信息科学与技术学院信息科学与技术学院_PI研究组_叶朝锋组
作者单位	1.Hangzhou Extremely Weak Magnetic Field Major Science and Technology Infrastructure Research Institute, Hangzhou, China 2.School of Information Science and Technology, Shanghaitech University, Shanghai, China
推荐引用方式 GB/T 7714	Yutong Wei,Chaofeng Ye. Steady-state visually evoked magnetic signal classification and BCI evaluation based on a convolutional neural network[J]. IEEE ACCESS,2024,PP(99).
APA	Yutong Wei,&Chaofeng Ye.(2024).Steady-state visually evoked magnetic signal classification and BCI evaluation based on a convolutional neural network.IEEE ACCESS,PP(99).
MLA	Yutong Wei,et al."Steady-state visually evoked magnetic signal classification and BCI evaluation based on a convolutional neural network".IEEE ACCESS PP.99(2024).