Towards Identifying Optimal Biased Feedback for Various User States and Traits in Motor Imagery BCI

Neural self-regulation is necessary for achieving control over brain-computer interfaces (BCIs). This can be an arduous learning process especially for motor imagery BCI. Various training methods were proposed to assist users in accomplishing BCI control and increase performance. Notably the use of biased feedback, i.e. non-realistic representation of performance. Benefits of biased feedback on performance and learning vary between users (e.g. depending on their initial level of BCI control) and remain speculative. To disentangle the speculations, we investigate what personality type, initial state and calibration performance (CP) could benefit from a biased feedback.

We conduct an experiment (n = 30 for 2 sessions). The feedback provided to each group (n = 10) is either positively, negatively or not biased.

Statistical analyses suggest that interactions between bias and: 1) workload, 2) anxiety, and 3) self-control significantly affect online performance. For instance, low initial workload paired with negative bias is associated to higher peak performances (86%) than without any bias (69%). High anxiety relates negatively to performance no matter the bias (60%), while low anxiety matches best with negative bias (76%). For low CP, learning rate (LR) increases with negative bias only short term (LR = 2%) as during the second session it severely drops (LR = -1%).

We unveil many interactions between said human factors and bias. Additionally, we use prediction models to confirm and reveal even more interactions.

This paper is a first step towards identifying optimal biased feedback for a personality type, state, and CP in order to maximize BCI performance and learning.