EEG Motor Execution Decoding via Interpretable Sinc-Convolutional Neural Networks

The decoding of brain signals is a fundamental component of a brain-computer interface. Despite the success of deep convolutional neural networks (CNNs) in other fields, only recently these techniques have been applied to electroencephalographic (EEG) signals. One drawback of CNNs is the lack of interpretation of the learned features. In this study we introduce for the first time a sinc-convolutional layer into a CNN for EEG motor execution decoding, allowing a straightforward interpretation of the learned kernels. Furthermore, we apply a gradient-based analysis to assess the most relevant EEG bands for each movement and the most relevant EEG electrodes exploited in these bands. In addition to a slight accuracy improvement from 91.9 to 92.4%, our results suggest that the band is the most relevant EEG band, with gradient-based scalp distributions well localized at specific subsets of electrodes.