A Hybrid Brain-Computer Interface Using Motor Imagery and SSVEP Based on Convolutional Neural Network
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The key to electroencephalography (EEG)-based brain-computer interface (BCI) lies in neural decoding, and its accuracy can be improved by using hybrid BCI paradigms, that is, fusing multiple paradigms. However, hybrid BCIs usually require separate processing processes for EEG signals in each paradigm, which greatly reduces the efficiency of EEG feature extraction and the generalizability of the model. Here, we propose a two-stream convolutional neural network (TSCNN) based hybrid brain-computer interface. It combines steady-state visual evoked potential (SSVEP) and motor imagery (MI) paradigms. TSCNN automatically learns to extract EEG features in the two paradigms in the training process, and improves the decoding accuracy by 25.4 MI mode, and 2.6 versatility of TSCNN is verified as it provides considerable performance in both single-mode (70.2 (95.6 applications of EEG-based BCI systems.
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