A new error-monitoring brain–computer interface based on reinforcement learning for people with autism spectrum disorders

Gabriel Pires

Abstract:

Objective. Brain–computer interfaces (BCIs) are emerging as promising cognitive training tools in
neurodevelopmental disorders, as they combine the advantages of traditional computerized
interventions with real-time tailored feedback. We propose a gamified BCI based on non-volitional
neurofeedback for cognitive training, aiming at reaching a neurorehabilitation tool for application
in autism spectrum disorders (ASDs). Approach. The BCI consists of an emotional facial expression
paradigm controlled by an intelligent agent that makes correct and wrong actions, while the user
observes and judges the agent’s actions. The agent learns through reinforcement learning (RL) an
optimal strategy if the participant generates error-related potentials (ErrPs) upon incorrect agent
actions. We hypothesize that this training approach will allow not only the agent to learn but also
the BCI user, by participating through implicit error scrutiny in the process of learning through
operant conditioning, making it of particular interest for disorders where error monitoring
processes are altered/compromised such as in ASD. In this paper, the main goal is to validate the
whole methodological BCI approach and assess whether it is feasible enough to move on to clinical
experiments. A control group of ten neurotypical participants and one participant with ASD tested
the proposed BCI approach. Main results. We achieved an online balanced-accuracy in ErrPs
detection of 81.6% and 77.1%, respectively for two different game modes. Additionally, all
participants achieved an optimal RL strategy for the agent at least in one of the test sessions.
Significance. The ErrP classification results and the possibility of successfully achieving an optimal
learning strategy, show the feasibility of the proposed methodology, which allows to move towards
clinical experimentation with ASD participants to assess the effectiveness of the approach as
hypothesized.