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Electroencephalography (EEG) plays a vital role in detecting how brainresponses to different stimulus. In this paper, we propose a novel Shallow-DeepAttention-based Network (SDANet) to classify the correct auditory stimulusevoking the EEG signal. It adopts the Attention-based Correlation Module (ACM)to discover the connection between auditory speech and EEG from global aspect,and the Shallow-Deep Similarity Classification Module (SDSCM) to decide theclassification result via the embeddings learned from the shallow and deeplayers. Moreover, various training strategies and data augmentation are used toboost the model robustness. Experiments are conducted on the dataset providedby Auditory EEG challenge (ICASSP Signal Processing Grand Challenge 2023).Results show that the proposed model has a significant gain over the baselineon the match-mismatch track.

Relate auditory speech to EEG by shallow-deep attention-based network