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CCA-Net: Zero-Shot SSVEP Classification via an Integration of Canonical Correlation Analysis and Deep Neural Network

文献类型：期刊论文

作者Deng, Yang1,2,3; Ji, Zhiwei4; Wang, Yijun5; Zhou, S. Kevin2,3,6
刊名IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
出版日期2025
卷号74页码:11
关键词Brain-computer interface (BCI) canonical cor relation analysis (CCA) deep neural network (DNN) steady-state visual evoked potential (SSVEP) steady-state visual evoked potential (SSVEP) zero-shot learning zero-shot learning zero-shot learning
ISSN号0018-9456
DOI10.1109/TIM.2025.3571139
英文摘要The accurate decoding of electroencephalographic (EEG) signals is a crucial foundation for brain-computer interface (BCI) applications. Among various decoding approaches, those that do not require calibration are particularly significant for advancing BCI technologies into everyday life, as they alleviate user fatigue by eliminating the need for user-specific training. In this study, we integrate a deep neural network (DNN) with canonical correlation analysis (CCA) to form the CCA-Net approach for decoding steady-state visually evoked potential (SSVEP) based BCI without user-specific calibration, i.e., at the zero-shot scenario. The CCA-Net aims to reduce the cross-subject domain gap for better transfer learning and make full use of the testing signal itself. Specifically, it adapts the cross-subject dual-domain fusion network (CSDuDoFN) by transferring the global multireference least-squares transformation (GMLST) coefficient from source subjects to both source and target data to reduce the cross-subject domain gap. In addition, CCA-Net enhances the transfer template-based CCA (tt-CCA) by introducing a subject-specific template to replace the sine-cosine reference template and adding an extra CCA coefficient to make full use of the testing signal itself, resulting in the Modified tt-CCA. Finally, the features extracted by CSDuDoFN and Modified tt-CCA are integrated to produce the final decoding result. Our approach leverages the strengths of both data-driven and model-driven methods and achieves state-of-the-art (SOTA) performance on three publicly available datasets, thus holding the potential to facilitate everyday BCI applications based on SSVEP. The reproducibility code is available at: https://github.com/Sungden/Zero-shot-SSVEP-classification
资助项目National Key Research and Development Program of China[2022YFF1202303] ; National Natural Science Foundation of China[62071447] ; National Natural Science Foundation of China[62271465] ; Project of Jiangsu Province Science and Technology Plan Special Fund in 2022 (Key Research and Development Plan Industry Foresight and Key Core Technologies)[BE2022064-1] ; Suzhou Basic Research Program[SYG202338] ; Open Fund Project of Guangdong Academy of Medical Sciences, China[YKY-KF202206]
WOS研究方向Engineering ; Instruments & Instrumentation
语种英语
WOS记录号WOS:001502506800032
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/41706]  
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.Harbin Inst Technol, Sch Life Sci & Technol, Harbin 150001, Heilongjiang, Peoples R China
2.Univ Sci & Technol China, Sch Biomed Engn, Div Life Sci & Med, Hefei 230026, Anhui, Peoples R China
3.Univ Sci & Technol China, Suzhou Inst Adv Res, Ctr Med Imaging Robot Analyt Comp & Learning MIRAC, Suzhou 215123, Jiangsu, Peoples R China
4.Nanjing Agr Univ, Coll Artificial Intelligence, Nanjing 210095, Jiangsu, Peoples R China
5.Chinese Acad Sci, Inst Semicond, State Key Lab Integrated Optoelect, Beijing 100083, Peoples R China
6.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,Ji, Zhiwei,Wang, Yijun,et al. CCA-Net: Zero-Shot SSVEP Classification via an Integration of Canonical Correlation Analysis and Deep Neural Network[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2025,74:11.
APA Deng, Yang,Ji, Zhiwei,Wang, Yijun,&Zhou, S. Kevin.(2025).CCA-Net: Zero-Shot SSVEP Classification via an Integration of Canonical Correlation Analysis and Deep Neural Network.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,74,11.
MLA Deng, Yang,et al."CCA-Net: Zero-Shot SSVEP Classification via an Integration of Canonical Correlation Analysis and Deep Neural Network".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 74(2025):11.

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来源：计算技术研究所

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