Real-time prediction of fast and slow delivery of mental commands in a motor imagery BCI: An entropy-based approach

Providing adaptive shared control for Brain-Computer Interfaces (BCIs) can result in better performance while reducing the user's mental workload. In this respect, online estimation of accuracy and speed of command delivery are important factors. This study aims at real-time differentiation between fast and slow trials in a motor imagery BCI. In our experiments, we refer to trials shorter than the median of trial lengths as “fast” trials and to those longer than the median as “slow” trials. We propose a classifier for real-time distinction between fast and slow trials based on estimates of the entropy rates for the first 2-3 s of the electroencephalogram (EEG). Results suggest that it can be predicted whether a trial is slow or fast well before a cutoff time. This is important for adaptive shared control especially because 55% to 75% of trials (for the five subjects in this study) are longer than that cutoff time.