  Recently, many efforts have been made to explore how the brain processes
speech using electroencephalographic (EEG) signals, where deep learning-based
approaches were shown to be applicable in this field. In order to decode speech
signals from EEG signals, linear networks, convolutional neural networks (CNN)
and long short-term memory networks are often used in a supervised manner.
Recording EEG-speech labeled data is rather time-consuming and laborious, while
unlabeled EEG data is abundantly available. Whether self-supervised methods are
helpful to learn EEG representation to boost the performance of EEG
auditory-related tasks has not been well explored. In this work, we first
propose a self-supervised model based on contrastive loss and reconstruction
loss to learn EEG representations, and then use the obtained pre-trained model
as a feature extractor for downstream tasks. Second, for two considered
downstream tasks, we use CNNs and Transformer networks to learn local features
and global features, respectively. Finally, the EEG data from other channels
are mixed into the chosen EEG data for augmentation. The effectiveness of our
method is verified on the EEG match-mismatch and EEG regression tasks of the
ICASSP2023 Auditory EEG Challenge.
