Deep learning applied to EEG data with different montages using spatial
  attention
Dung Truong Muhammad Abdullah Khalid Arnaud Delorme
The ability of Deep Learning to process and extract relevant information in
complex brain dynamics from raw EEG data has been demonstrated in various
recent works. Deep learning models, however, have also been shown to perform
best on large corpora of data. When processing EEG, a natural approach is to
combine EEG datasets from different experiments to train large deep-learning
models. However, most EEG experiments use custom channel montages, requiring
the data to be transformed into a common space. Previous methods have used the
raw EEG signal to extract features of interest and focused on using a common
feature space across EEG datasets. While this is a sensible approach, it
underexploits the potential richness of EEG raw data. Here, we explore using
spatial attention applied to EEG electrode coordinates to perform channel
harmonization of raw EEG data, allowing us to train deep learning on EEG data
using different montages. We test this model on a gender classification task.
We first show that spatial attention increases model performance. Then, we show
that a deep learning model trained on data using different channel montages
performs significantly better than deep learning models trained on fixed 23-
and 128-channel data montages.