Exploration-based feedback for BCI training: a case study with an adolescent with paraplegia

Brain-computer interface (BCI) inefficiency limits clinical utilization of BCIs, as many users struggle to produce consistent machine-recognizable electroencephalography (EEG) patterns for reliable control. While training can improve BCI performance for most users, the required duration and intensity may hinder BCI accessibility. Prior BCI and motor learning research suggests that feedback enabling efficient exploration of different task strategies may enhance training.

An eight-session BCI training case study was completed with an adolescent participant with paraplegia. To support training, a novel feedback system that visualized EEG signal pattern states identified via K-means clustering within the EEG covariance space. Unlike common classifier feedback, this interface presented the EEG signal patterns produced throughout each trial, allowing the participant to explore strategies that yielded task-specific pattern states.

The participant was initially a low-performing user and showed little progress across the first five sessions. After transitioning to a simplified feedback mode emphasizing deviation from resting state patterns in the sixth session, the participant displayed significant improvement in task-related physiological signal discriminability. Post-training analysis, however, revealed that this improvement was partially attributable to electromyography (EMG) activity from cranial muscles.

Although the observed gains were not solely attributable to neuro-cortical signal modulation, the case study highlights the potential of simplified feedback to support task exploration in low-performing users and presents potential implications for hybrid EEG-EMG BCIs for relevant clinical populations.