  Deep reinforcement learning (RL) algorithms enable the development of fully
autonomous agents that can interact with the environment. Brain-computer
interface (BCI) systems decipher human implicit brain signals regardless of the
explicit environment. In this study, we integrated deep RL and BCI to improve
beneficial human interventions in autonomous systems and the performance in
decoding brain activities by considering environmental factors. Shared autonomy
was allowed between the action command decoded from the electroencephalography
(EEG) of the human agent and the action generated from the twin delayed DDPG
(TD3) agent for a given environment. Our proposed copilot control scheme with a
full blocker (Co-FB) significantly outperformed the individual EEG (EEG-NB) or
TD3 control. The Co-FB model achieved a higher target approaching score, lower
failure rate, and lower human workload than the EEG-NB model. The Co-FB control
scheme had a higher invisible target score and level of allowed human
intervention than the TD3 model. We also proposed a disparity d-index to
evaluate the effect of contradicting agent decisions on the control accuracy
and authority of the copilot model. We found a significant correlation between
the control authority of the TD3 agent and the performance improvement of human
EEG classification with respect to the d-index. We also observed that shifting
control authority to the TD3 agent improved performance when BCI decoding was
not optimal. These findings indicate that the copilot system can effectively
handle complex environments and that BCI performance can be improved by
considering environmental factors. Future work should employ continuous action
space and different multi-agent approaches to evaluate copilot performance.
