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

Nicolas Ivanov1,2, Tom Chau3,4

  • 1Institute of Biomedical Engineering, University of Toronto, 164 College St, Toronto, ON, M5S 3G9, Canada.

Journal of Neuroengineering and Rehabilitation
|July 10, 2026
PubMed
Summary

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A novel feedback system improved brain-computer interface (BCI) control for a low-performing user by enabling strategy exploration. Simplified feedback, emphasizing deviations from resting states, enhanced electroencephalography (EEG) signal discriminability, though electromyography (EMG) also contributed.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Brain-computer interface (BCI) utilization is limited by user inefficiency in generating consistent electroencephalography (EEG) patterns.
  • Extensive training can improve BCI performance but may reduce accessibility due to duration and intensity.

Purpose of the Study:

  • To investigate the efficacy of a novel feedback system for enhancing BCI training in a low-performing user.
  • To explore how visualizing EEG signal states facilitates strategy discovery during BCI task performance.

Main Methods:

  • An eight-session BCI training case study was conducted with an adolescent participant with paraplegia.
  • A novel feedback system visualized EEG signal states identified via K-means clustering, allowing users to explore task strategies.
Keywords:
BCI inefficiencyBrain–computer interfaceEEGEMGHybrid BCIPediatric BCIUser training

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  • Feedback was simplified in later sessions to emphasize deviation from resting state patterns.

    • Main Results:

    • The participant showed minimal progress in the initial five sessions.
    • Significant improvement in task-related physiological signal discriminability was observed after transitioning to simplified feedback.
    • Post-training analysis indicated that electromyography (EMG) activity from cranial muscles partially contributed to the observed gains.

    Conclusions:

    • Simplified feedback can aid low-performing BCI users in exploring task strategies.
    • Observed performance gains were not solely due to neuro-cortical modulation.
    • Findings suggest potential for hybrid EEG-EMG BCIs in clinical applications.