1(College of Computer and Data Science,Putian University, Putian 351100, Fujian,China) 2(School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China) 3(College of Intelligent Manufacturing, Putian University, Putian 351100, Fujian,China)
Abstract：Aiming to solve the problem of insufficient ability of feature extraction by the traditional motor imagery decoding algorithm for long time series and low recognition rate of EEG signals, a novel CNN wavelet decomposition spectral encoder EEG decoding algorithm was proposed in this work, based on the fusion idea of convolutional neural network and transformer encoder and combining wavelet decomposition and spectral attention. First, the convolution module was constructed for local feature extraction, and the multi-level sub-features were obtained by one-level wavelet decomposition. Based on the transformer encoder, spectral attention was introduced to replace the traditional multi-head attention, and a new FFT-Former encoder was constructed followed by the input of multi-level sub-features into the spectrum attention for frequency domain modeling. Next, the time-domain features obtained by wavelet reconstruction technology were input into the feedforward network to enable the model to learn more complex feature dependencies. Finally, the feature information of convolution module and FFT-Former was fused to design a classifier to achieve low computational complexity and high accuracy EEG signal recognition. Verified by public datasets, the average accuracy of motor imagery decoding of CWFT network model on BCI Competition IV-2a and BCI Competition IV-2b reached 85.65% and 89.54% respectively, and the Kappa coefficient reached 80.86% and 79.07% respectively. By comparing with other comparison algorithms, we demonstrated that the proposed algorithm had excellent classification performance and provided a new idea for the construction of motor imagery brain-computer interface.
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