SSVEP Signal Recognition Based on Transfer Learning and Residual Networks
A residual network SSVEP signal recognition method based on parameter sharing transfer learning is proposed to address the challenges of adapting to individual differences,poor recognition stability,and low accuracy in target recognition of steady-state visual evoked potential(SSVEP)signals in EEG signals.Firstly,the multi-channel SSVEP signals are transformed into wavelet coefficients using discrete wavelet transform as the input feature set to-gether with the pre-transformed signals;thus,the extracted features are more abundant.Secondly,a residual network fused with a spatial attention mechanism is established,and two SSVEP signal datasets,including 105 individuals,provided by the Tsinghua University brain-computer interface are used to achieve a cross-task and cross-individual transfer.The network trained on the source domain is migrated to the target network block by block to obtain the ap-propriate transfer block,and the recognition results are obtained by connecting the 2 residual blocks and pattern rec-ognition units after the transfer.The total recognition rate in the test set reaches 84.2％ under a 1s time-window with no intersection between training and test individuals.Thus,the proposed method is characterized by relatively high in-dividual adaptability,accuracy,and robustness in SSVEP signal recognition.
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