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    This study introduces hyperCSP, a novel method for brain-computer interfacing (BCI) that improves motor task classification by analyzing multiple subjects' brain data. It achieves high accuracy even with interfering tasks, reducing BCI training errors.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Machine Learning

    Background:

    • Common Spatial Patterns (CSP) is a key feature extraction technique in brain-computer interfacing (BCI).
    • Existing CSP methods face challenges in multi-subject scenarios with complex motor tasks and interference.
    • Neurological studies often require robust methods for analyzing simultaneous brain activity from multiple individuals (hyperscanning).

    Purpose of the Study:

    • To develop an advanced CSP formulation, termed hyperCSP, for enhanced feature extraction in multi-subject BCI.
    • To effectively isolate common motor tasks between simultaneously recorded subjects.
    • To mitigate the impact of spurious or undesired tasks on BCI performance.

    Main Methods:

    • A novel hyperCSP formulation is proposed, integrating individual covariance and mutual correlation matrices from multi-subject electroencephalograms (EEG).
    • The hyperCSP method was applied to analyze motor-related hyperscanning data.
    • Classification was performed using hyperCSP features combined with a support vector machine (SVM) classifier.

    Main Results:

    • The hyperCSP method demonstrated effective isolation of common motor tasks among multiple subjects.
    • Achieved a classification accuracy of 81.82% over 8 trials, even with significant undesired task interference.
    • The technique offers satisfactory classification performance with reduced data size and computational complexity.

    Conclusions:

    • HyperCSP presents a promising advancement for feature extraction in multi-task BCI applications.
    • This method has the potential to significantly reduce training errors in complex BCI scenarios.
    • The publicly available motor-related hyperscanning dataset will facilitate further research in the field.