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Modification of cortical activation pattern after long-term BCI training and its impact on decoding model

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    Summary

    Inter-session variability in brain-computer interfaces (BCIs) impacts model performance and reflects patient adaptation. This study quantifies physiological drift and its link to BCI performance, aiding in developing more reliable rehabilitation systems.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Rehabilitation Medicine

    Background:

    • Inter-session variability in brain signals is a significant challenge for brain-computer interfaces (BCIs).
    • This variability affects model performance and may indicate long-term brain adaptation in patients undergoing BCI training.
    • Understanding physiological drift is crucial for improving BCI stability and effectiveness.

    Purpose of the Study:

    • To investigate physiological drift in BCIs by analyzing brain activity evolution across sessions.
    • To quantify the relationship between physiological drift and BCI decoder performance.
    • To explore the potential of BCI-induced brain modifications for rehabilitation.

    Main Methods:

    • Analysis of spatial patterns of synchronization and desynchronization across a wide frequency range.
    • Application of a linear regression model to quantify drift and residual variability.
    • Correlation analysis between physiological variability and decoder performance.

    Main Results:

    • Quantification of physiological drift and its impact on BCI performance.
    • Demonstration of coherence between physiological changes and decoder outcomes.
    • Identification of BCI-driven long-term modifications in brain activation patterns.

    Conclusions:

    • Physiological drift significantly impacts BCI performance, necessitating strategies for adaptation.
    • BCI training can induce measurable long-term changes in brain activity, supporting its role in rehabilitation.
    • This research contributes to developing more robust and reliable BCI systems for clinical applications.