Benefits of deep learning classification of continuous noninvasive brain-computer interface control

Objective. Noninvasive brain-computer interfaces (BCIs) assist paralyzed patients by providing access to the world without requiring surgical intervention. Prior work has suggested that EEG motor imagery based BCI can benefit from increased decoding accuracy through the application of deep learning methods, such as convolutional neural networks (CNNs). Approach. Here, we examine whether these improvements can generalize to practical scenarios such as continuous control tasks (as opposed to prior work reporting one classification per trial), whether valuable information remains latent outside of the motor cortex (as no prior work has compared full scalp coverage to motor only electrode montages), and the existing challenges to the practical implementation of deep-learning based continuous BCI control. Main results. We report that: (1) deep learning methods significantly increase offline performance compared to standard methods on an independent, large, and longitudinal online motor imagery BCI dataset with up to 4-classes and continuous 2D feedback; (2) our results suggest that a variety of neural biomarkers for BCI, including those outside the motor cortex, can be detected and used to improve performance through deep learning methods, and (3) tuning neural network output will be an important step in optimizing online BCI control, as we found the CNN models trained with full scalp EEG also significantly reduce the average trial length in a simulated online cursor control environment. Significance. This work demonstrates the benefits of CNNs classification during BCI control while providing evidence that electrode montage selection and the mapping of CNN output to device control will be important design choices in CNN based BCIs.