基于联邦学习的稳态视觉诱发电位频率识别算法研究

doi:10.3969/j.issn.0258-8021.2025.05.007

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Abstract Steady-state visual evoked potential (SSVEP) is widely used in brain-computer interface (BCI) applications due to its high signal-to-noise ratio and stability. Currently, frequency recognition algorithms for SSVEP based on deep learning are facing the challenge of insufficient data. Effective training models using multi-center data has emerged as a potential solution. In this paper, a federated learning algorithm framework FLSSVEP for SSVEP recognition algorithm was proposed, which realized the joint training of models on different clients, and avoided the violation of data privacy while using more training data, so as to alleviate the impact of insufficient data or data dispersion on the model training. To verify the effectiveness of the proposed algorithm framework, comprehensive experiments were conducted on two public datasets of Benchmark and BETA by using EEGNet, CCNN, and SSVEP Former as the baseline models. Experimental results showed that the proposed FLSSVEP led to an average improvement of 33.82% in classification accuracy for these baseline models on one client, while the other two clients achieved average improvements of 8.13% and 6.05% in classification accuracy, respectively, better than those obtained from traditional local data training methods. This study demonstrated the effectiveness of federated learning in designing algorithms for SSVEP-based BCI, providing a theoretical reference for future research.

Key words： brain-computer interface ssvep federated learning frequency recognition transfer learning

Received: 18 September 2024

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R318

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	Articles by authors
	Li Jinfei
	Chen Jianbo
	Zhang Yangsong
	Xu Peng

Cite this article:

Li Jinfei,Chen Jianbo,Zhang Yangsong, et al. Research on Federated Learning-Based SSVEP Frequency Recognition Algorithm[J]. Chinese Journal of Biomedical Engineering, 2025, 44(5): 570-578.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2025.05.007 OR http://cjbme.csbme.org/EN/Y2025/V44/I5/570

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