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Assessment and Communication for People with Disorders of Consciousness
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A Non-Invasive, MRCP-Based BCI for Online Communication.

Markus R Crell, Kyriaki Kostoglou, Patrick Suwandjieff

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |February 20, 2026
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    Summary
    This summary is machine-generated.

    This study introduces a brain-computer interface (BCI) using electroencephalography (EEG) to detect movement intentions for communication. The system enables users with motor impairments to control spelling applications effectively.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Rehabilitation Technology

    Background:

    • Individuals with severe motor impairments often lack reliable communication methods.
    • Brain-computer interfaces (BCIs) offer a promising avenue for restoring communication abilities.
    • Existing BCIs may require extensive calibration or external cues, limiting intuitive use.

    Purpose of the Study:

    • To develop and evaluate a novel BCI system for asynchronous detection of movement intent from EEG signals.
    • To create a task-agnostic framework for controlling spelling applications, emulating a button press.
    • To assess the system's performance in enabling communication for individuals with motor impairments.

    Main Methods:

    • A BCI system was developed to detect movement-related cortical potentials (MRCPs) from EEG data.
    • The system asynchronously detects self-initiated movements without external cues.
    • A spelling interface using row-column scanners (3x3 and 5x5) was controlled by the BCI for typing five-letter words.

    Main Results:

    • The MRCP-based classifier demonstrated consistent detection performance across different interface configurations.
    • Participants achieved an average true positive rate (TPR) of 54.4% and correctly selected 60.9% of target characters.
    • A five-letter word was successfully spelled in 41.7% of attempts, with low false positive rates.

    Conclusions:

    • The developed BCI system shows potential as a non-invasive communication aid for individuals with motor impairments.
    • The task-agnostic, MRCP-based approach offers robust and adaptable control for various applications.
    • This research lays the groundwork for future home-use BCIs with intuitive, voluntary control and minimal calibration.