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Deep transfer learning-based SSVEP frequency domain decoding method
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DOI：10.1016/j.bspc.2023.105931.png)
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Objective: Improving the decoding accuracy and information transfer rate (ITR) of a steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI) system and narrowing the inter-subject variance are key to the application of the SSVEP-BCI system. To this end, we proposed a deep transfer learning-based SSVEP frequency domain decoding method to improve the decoding performance. Methods: Input data representations with rich spatial and frequency domain features were extracted using filter bank and zero-padding-based fast Fourier transform techniques. A concise and efficient 3-dimensional convolutional neural network (3DCNN) model was designed for feature extraction and decoding of the input data. A transfer learning strategy was proposed to further improve the decoding accuracy and narrow the inter-subject variance. Results: Our proposed 3DCNN achieves 89.35 % average classification accuracy and 173.02 bits/min ITR on the benchmark dataset with 1 s signal length. On our laboratory dataset, the average classification accuracy and ITR of 3DCNN reach 88.75 % and 120.33 bits/min, respectively, when the signal length is 0.6 s. Conclusions: In this study, we experimentally confirmed the effectiveness and superiority of our proposed method for SSVEP decoding, which provides a promising decoding method for the application of SSVEP-BCI.
Keyword:
Steady-state visual evoked potential
Brain-computer interface
Fast fourier transform
Deep learning
Transfer learning
期刊
IF:
4.9
论文数:
1.0W
被引数:
2.4W
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