  Deep neural networks (DNN) have become increasingly utilized in
brain-computer interface (BCI) technologies with the outset goal of classifying
human physiological signals in computer-readable format. While our present
understanding of DNN usage for BCI is promising, we have little experience in
deciphering neural events from dynamic freely-mobile situations. Using an
improved version of EEGNet, our goal was to classify cognitive events from
electroencephalography (EEG) signals while subjects simultaneously walked on a
treadmill, sometimes while carrying a rucksack equivalent to 40% of their body
weight. Walking subjects simultaneously performed a visual oddball target
detection task, eliciting the P300 event-related potential (ERP), which then
served as the DNN classification target. We found the base EEGNet to reach
classification levels well above chance, with similar performance to previously
reported P300 results. We found performance to be robust to noise, with
classification similar for walking and loaded walking, with respect to standard
seated condition with minimal movement. With additional architectural search
and tuning to the EEGNet model (termed Cog-Neuro, herein; CN-EEGNet), we
reached classification accuracy of greater than 95%, similar to previously
reported state of the art levels in seated P300 tasks. To our knowledge, these
results are the first documented implementation of a DNN for the classification
of cognitive neural state during dual-task walking. The classification of one's
ongoing cognitive state during a demanding physical task establishes the
utility for BCI in complex environments.
