Abstract：The motor imagery (MI) EEG signal itself is a signal sequence consisting of a set of long and continuous feature values. Traditional Transformers cannot capture dependencies between longer sequences, and setting fixed length sequences can lead to fragmentation issues, so further adjustments and optimization are needed. To address the above issues, a fragment reuse loop mechanism and a relative position encoding mechanism for fragment information have been added to traditional Transformers. This can enable the Transformer to learn feature information from longer feature sequences. At the same time, it can also solve problems such as confusion and reuse of positional encoding information between reused fragments. Subsequently, parallel multi branch CNN was used to further capture local EEG features. Finally, the performance of the improved Transformer model was evaluated using the competition dataset 2008 BCI-Competition 2A. The results showed that, without any manual feature extraction, the average accuracy and kappa value of the improved Transformer model for the four classification dataset were 94.27% and 87.34%, respectively.
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2023, Vol. 44 
