Automatically Revealing Multi-Domain EEG Signatures Related to Motor Imagery Using a Fully-Interpretable Convolutional Neural Network

Electroencephalographic (EEG) signals are widely used to analyze brain functions. EEG analysis may benefit from interpretable convolutional neural networks (CNNs), realizing data-driven analysis pipelines capable of automatically learning the most meaningful EEG features by exploiting the entire information contained in the EEG. However, the available solutions enable a direct analysis of the learned EEG features mainly within only one domain (frequency domain), and it is not available yet a fully-interpretable CNN that eases the interpretation of features living in all the main domains characterizing the EEG, that is, frequency, spatial, and temporal domains. This hinders the application of neural networks to EEG analysis. Here, we propose a fully-interpretable CNN learning features in the frequency, spatial, and temporal domains that can be easily interpreted once the network is trained. The network processes the input by learning the optimal bank of bandpass filters (modeled with generalized Gaussian functions), and then the optimal combination of EEG channels. Finally, the network learns how to optimally recombine time samples. We test our approach to motor imagery decoding. The network significantly outperforms existing state-of-the-art interpretable CNNs, while maintaining the highest degree of feature interpretability spanning across multiple domains. Finally, the analysis conducted on network features matched the known correlates of motor imagery in the frequency, spatial and temporal domains. Our network represents a comprehensive analysis tool that could be used to automatically extract, in multiple domains, the most salient EEG features for the brain states under analysis.