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Relating speech to EEG holds considerable importance but challenging. In thisstudy, deep convolutional network was employed to extract spatiotemporalfeatures from EEG data. Self-supervised speech representation and contextualtext embedding were used as speech features. Contrastive learning was used torelated EEG features to speech features. The experimental results demonstratethe benefits of using self-supervised speech representation and contextual textembedding. Through feature fusion and model ensemble, an accuracy of 60.29% wasachieved, and the performance was ranked as No.2 in Task1 of the Auditory EEGChallenge (ICASSP 2024).

Self-supervised speech representation and contextual text embedding for match-mismatch classification with EEG recording