Deep learning applied to EEG data with different montages using spatial attention

Deep learning models are capable of extracting task-relevant information in complex brain dynamics from large corpora of raw EEG data. Given the small size of EEG datasets and the heterogeneity of each dataset’s channel montage, aggregating EEG datasets for large-scale training of deep learning models often requires a way to harmonize different channel locations effectively. Previous methods have focused on extracting features from raw EEG and projecting them onto a common space. However, these approaches underexploit the potential richness of EEG raw data. Here, we proposed a method to train deep learning models on raw EEG data with different channel montages using spatial attention on electrode coordinates. We test this approach on a gender classification task. We first show that increasing channel montage density increases performance. We then show that spatial attention increases model performance for different channel montage densities. Then, we show that in nonuniform montage settings, deep learning models trained on combined data with different channel montages performs significantly better than deep learning models trained on individual datasets with fixed montages. This work opens up the potential for future application of deep learning models on datasets collected by different labs across different recording settings.