Identifying factors influencing the outcome of BCI-based post stroke motor rehabilitation towards its personalization with Artificial Intelligence

Stroke is the leading cause of complex disability in adults. The prevalence of motor deficit and cognitive impairment after stroke is high and persistent. The most common consequence is the hemiparesis of the contralateral upper limb, with over 80% of stroke patients suffering from this condition acutely and over 40% chronically. Brain-Computer Interfaces (BCI) based on motor imagery have shown promising results in post-stroke motor recovery. However, this approach does not work for all patients, and even when it works, shows vastly different effectiveness across patients. It thus needs to be improved. This could be achieved by personalizing the BCI-based Motor Rehabilitation (MR) program to each patient, notably by personalizing the employed Artificial Intelligence (AI) models used. To do this, it is necessary to first identify the predictive factors of successful BCI-based motor rehabilitation. In fact, very little research has addressed the question of factors that influence post-stroke BCI-based MR. Thus, in this paper, we present a survey of the literature about the factors related to successful use of BCIs in general and then the factors that are associated to post-stroke motor recovery, to identify the various factors that could influence BCI-based post-stroke MR. We then discuss how such factors could be taken into account in order to develop new AI algorithms for personalized post-stroke BCI-based MR.