  Building machine learning models using EEG recorded outside of the laboratory
setting requires methods robust to noisy data and randomly missing channels.
This need is particularly great when working with sparse EEG montages (1-6
channels), often encountered in consumer-grade or mobile EEG devices. Neither
classical machine learning models nor deep neural networks trained end-to-end
on EEG are typically designed or tested for robustness to corruption, and
especially to randomly missing channels. While some studies have proposed
strategies for using data with missing channels, these approaches are not
practical when sparse montages are used and computing power is limited (e.g.,
wearables, cell phones). To tackle this problem, we propose dynamic spatial
filtering (DSF), a multi-head attention module that can be plugged in before
the first layer of a neural network to handle missing EEG channels by learning
to focus on good channels and to ignore bad ones. We tested DSF on public EEG
data encompassing ~4,000 recordings with simulated channel corruption and on a
private dataset of ~100 at-home recordings of mobile EEG with natural
corruption. Our proposed approach achieves the same performance as baseline
models when no noise is applied, but outperforms baselines by as much as 29.4%
accuracy when significant channel corruption is present. Moreover, DSF outputs
are interpretable, making it possible to monitor channel importance in
real-time. This approach has the potential to enable the analysis of EEG in
challenging settings where channel corruption hampers the reading of brain
signals.
