Exploration of using ‘distance-to-bound’ to manipulate the difficulty during motor imagery BCI training after stroke—a clinical two-cases study

<i>Objective</i>. Motor imagery-based Brain-Computer interfaces (MI-BCIs) is a promising technology for neurorehabilitation after stroke. However, many face challenges in using a BCI because they fail to produce discriminable patterns in their brain activity. Personalizing the BCI task difficulty could help the learning process of these users but there is currently very limited knowledge on which methods can be used online. Our aim was to explore a distance-to-bound (DTB) approach for adapting MI BCI task difficulty in real time.<i>Approach</i>. Two chronic stroke patients performed 12 BCI training sessions over 4 weeks during which they performed MI of open- and close hand movements and received continual visual feedback based on multivariate decoding of ongoing electroencephalogram (EEG) activity. We increased the difficulty and maintained it by adapting it in real time based on DTB decoding metrics and, by using a multiple-session design, we investigated the stability of this approach and how it related to MI-related EEG activity of each patient.<i>Main results</i>. We show that patients had to produce stronger alpha and beta event-related desynchronization/synchronization (ERDS) pattern across the sensorimotor cortical areas of the brain to receive positive feedback. In addition, we show that the online adaptation converged within sessions as well as accommodating for drift in the data both within and between sessions. We suggest that the DTB approach can effectively be used to control BCI task difficulty which could, in future BCIs, serve as a potential tool to guide patients to produce functionally relevant activity patterns. However stronger sensorimotor ERDS did not correlate to improved motor function in one of our two patients. As this result is observational and cannot support causal claims, it exemplifies the need to individually tailor the translation of DTB outputs to feedback considering the stroke lesion and EEG activity profile of the specific patient.<i>Significance</i>. This study provides valuable insights and considerations for BCI difficulty adaptation in the aim of developing more effective training protocols in BCI-based stroke rehabilitation.<b>Trial registration</b>The study was registered at clinicaltrials.gov (NCT03994042) and complied with local rules and regulations according to the Swedish Ethical Review Authority (dnr. 2019-01577).