  Relating speech to EEG holds considerable importance but is challenging. In
this study, a deep convolutional network was employed to extract spatiotemporal
features from EEG data. Self-supervised speech representation and contextual
text embedding were used as speech features. Contrastive learning was used to
relate EEG features to speech features. The experimental results demonstrate
the benefits of using self-supervised speech representation and contextual text
embedding. Through feature fusion and model ensemble, an accuracy of 60.29% was
achieved, and the performance was ranked as No.2 in Task 1 of the Auditory EEG
Challenge (ICASSP 2024). The code to implement our work is available on Github:
https://github.com/bobwangPKU/EEG-Stimulus-Match-Mismatch.
