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    High-density Near Infrared Spectroscopy (NIRS) effectively classifies working memory load. This brain-computer interface (BCI) shows promise for assessing cognitive states using NIRS data.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Cognitive Science

    Background:

    • Working memory assessment is crucial for understanding cognitive function.
    • Near Infrared Spectroscopy (NIRS) offers a non-invasive method for brain activity monitoring.
    • High-density NIRS provides improved spatial resolution for brain signal detection.

    Purpose of the Study:

    • To evaluate the single-trial classification performance of high-density NIRS data.
    • To assess the feasibility of using NIRS-based Brain-Computer Interface (BCI) for working memory load.
    • To differentiate between working memory tasks of varying difficulty and an idle state.

    Main Methods:

    • Collected high-density NIRS data from 11 healthy subjects during n-back tasks (easy n=1, hard n=3) and idle conditions.
    • Applied common average reference spatial filtering and single-trial baseline referencing for feature extraction.
    • Utilized mutual information-based feature selection and a support vector machine classifier with 5x5-fold cross-validation.

    Main Results:

    • Achieved classification accuracies of 72.7% (hard vs. easy), 68.0% (easy vs. idle), and 84.0% (hard vs. idle).
    • Demonstrated successful single-trial classification of cognitive states based on NIRS signals.
    • Indicated robust performance across different task difficulty comparisons.

    Conclusions:

    • High-density NIRS-based BCI is feasible for assessing working memory load.
    • NIRS technology can effectively discriminate between different levels of cognitive demand.
    • This approach holds potential for non-invasive cognitive state monitoring.