Presented by Serafeim Perdikis, University of Essex
Adaptation and learning in non-invasive brain-computer interaction
Non-invasive brain-computer interface (BCI) has entered an era of relative technological maturity, where BCI prototypes are increasingly deployed as assistive devices and rehabilitation interventions, or towards able-bodied user applications like entertainment and driving assistance. The main obstacles hindering the translation and industrialization of non-invasive BCI are the intense performance fluctuations that often impede brain-actuated device operation, and the inability of large portions of prospective users to exhibit adequate BCI control after conventional user training. Adaptive BCI algorithms and the co-adaptive (human and machine) symbiotic regimes they give rise to, have been early proposed as a remedy to both these issues. In this session, we will first identify the machine learning and other technical user-training challenges that need to be addressed towards effective BCI adaptation, with references to possible solutions that have been proposed in the recent non-invasive BCI literature, and their limitations. Second, we will shift the focus to the–often, overlooked–topic of subject learning during co-adaptation, taking a critical viewpoint of the state-of-the-art and leveraging the evidence of recent works in order to pinpoint the currently missing links towards a truly mutual learning framework. Ultimately, this session aspires to survey and conceptualize the main theoretical and practical caveats of non-invasive, adaptive BCI, thus providing a tentative roadmap towards co-adaptive training able to facilitate both learning agents of the BCI loop, to accommodate their interactions, to enable universal BCI accessibility and, through that, to fuel translational and commercial BCI applicability.