TRCA-Net: using TRCA filters to boost the SSVEP classification with convolutional neural network

doi:10.1088/1741-2552/ace380

	TRCA-Net: using TRCA filters to boost the SSVEP classification with convolutional neural network
	Deng, Yang 1,2; Sun, Qingyu 3; Wang, Ce 4; Wang, Yijun 3; Zhou, S. Kevin 1,2,4
	2023-08-01
发表期刊	JOURNAL OF NEURAL ENGINEERING
ISSN	1741-2560
卷号	20 期号:4 页码:12
摘要	Objective. The steady-state visual evoked potential (SSVEP)-based brain-computer interface has received extensive attention in research due to its simple system, less training data, and high information transfer rate. There are currently two prominent methods dominating the classification of SSVEP signals. One is the knowledge-based task-related component analysis (TRCA) method, whose core idea is to find the spatial filters by maximizing the inter-trial covariance. The other is the deep learning-based approach, which directly learns a classification model from data. However, how to integrate the two methods to achieve better performance has not been studied before.Approach. In this study, we develop a novel algorithm named TRCA-Net (TRCA-Net) to enhance SSVEP signal classification, which enjoys the advantages of both the knowledge-based method and the deep model. Specifically, the proposed TRCA-Net first performs TRCA to obtain spatial filters, which extract task-related components of data. Then the TRCA-filtered features from different filters are rearranged as new multi-channel signals for a deep convolutional neural network (CNN) for classification. Introducing the TRCA filters to a deep learning-based approach improves the signal-to-noise ratio of input data, hence benefiting the deep learning model.Main results. We evaluate the performance of TRCA-Net using two publicly available large-scale benchmark datasets, and the results demonstrate the effectiveness of TRCA-Net. Additionally, offline and online experiments separately testing ten and five subjects further validate the robustness of TRCA-Net. Further, we conduct ablation studies on different CNN backbones and demonstrate that our approach can be transplanted into other CNN models to boost their performance.Significance. The proposed approach is believed to have a promising potential for SSVEP classification and promote its practical applications in communication and control. The code is available at https://github.com/Sungden/TRCA-Net.
关键词	brain-computer interface (BCI) steady-state visual evoked potential (SSVEP) task-related component analysis (TRCA) convolutional neural network (CNN)
DOI	10.1088/1741-2552/ace380
收录类别	SCI
语种	英语
资助项目	National Key Ramp;D Program of China[2022YFF1202303] ; National Natural Science Foundation of China[62071447] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB32040200]
WOS研究方向	Engineering ; Neurosciences & Neurology
WOS类目	Engineering, Biomedical ; Neurosciences
WOS记录号	WOS:001025030700001
出版者	IOP Publishing Ltd
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21254
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wang, Yijun; Zhou, S. Kevin
作者单位	1.Univ Sci & Technol China, Sch Biomed Engn, Div Life Sci & Med, Hefei 230026, Anhui, Peoples R China 2.Univ Sci & Technol China, Suzhou Inst Adv Res, Ctr Med Imaging Robot Analyt Comp & Learning MIRAC, Suzhou 215123, Jiangsu, Peoples R China 3.Chinese Acad Sci, Inst Semicond, State Key Lab Integrated Optoelect, Beijing 100083, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Deng, Yang,Sun, Qingyu,Wang, Ce,et al. TRCA-Net: using TRCA filters to boost the SSVEP classification with convolutional neural network[J]. JOURNAL OF NEURAL ENGINEERING,2023,20(4):12.
APA	Deng, Yang,Sun, Qingyu,Wang, Ce,Wang, Yijun,&Zhou, S. Kevin.(2023).TRCA-Net: using TRCA filters to boost the SSVEP classification with convolutional neural network.JOURNAL OF NEURAL ENGINEERING,20(4),12.
MLA	Deng, Yang,et al."TRCA-Net: using TRCA filters to boost the SSVEP classification with convolutional neural network".JOURNAL OF NEURAL ENGINEERING 20.4(2023):12.