Decoding Neural Signals with a Compact and Interpretable Convolutional Neural Network

Brain-computer interfaces (BCIs) decode information from neural activity and send it to external devices. In recent years, we have seen an emergence of new algorithms for BCI decoding. Here we propose a compact architecture for adaptive decoding of electrocorticographic (ECoG) data into finger kinematics. We also describe a theoretically justified approach to interpreting the spatial and temporal weights in the architectures that combine adaptation in both space and time, such as ours. In hese architectures the weights are optimized not only for decoding of target signals but also for tuning away from the interfering sources, in both the spatial and the frequency domains. When applied to a dataset taken from the repository of Berlin BCI IV competition, our architecture outperformed the competition winners without the need for feature selection. Moreover, by looking at the architecture weights we could explain in physiological terms how our algorithm decodes spatial and temporal parameters of finger kinematics. As such, the proposed architecture offers a good decoder and a tool for investigating neural mechanisms of motor control.