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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Brain-computer interfaces (BCIs) offer alternative communication and control pathways.
    • Current BCIs often rely on visual or motor imagery, limiting user engagement.
    • Somatosensory attentional orientation (SAO) presents a novel paradigm for BCI control.

    Purpose of the Study:

    • To investigate the performance of a multi-class BCI system based on SAO.
    • To analyze the electroencephalography (EEG) patterns associated with different SAO tasks.
    • To evaluate the classification accuracy of the SAO-based BCI.

    Main Methods:

    • Participants performed four SAO tasks: left hand (SAO-LF), right hand (SAO-RT), bilateral (SAO-BI), and idle (SAO-ID).
    • EEG signals were analyzed for event-related desynchronization and synchronization (ERD/ERS) patterns.
    • Classification accuracy was determined for two, three, and four classes using selected frequency bands.

    Main Results:

    • Distinct somatosensory cortical activation patterns were observed for each SAO task.
    • SAO-LF and SAO-RT showed contralateral ERD; SAO-BI showed bilateral ERD; SAO-ID showed bilateral ERS.
    • Classification accuracies reached 85.2% for two classes, 69.5% for three, and 55.9% for four classes on a single trial basis.

    Conclusions:

    • The SAO paradigm effectively differentiates brain activity related to somatosensory attention.
    • This multi-class BCI system demonstrates potential for stimulus-independent control.
    • SAO offers a promising new approach for developing advanced BCIs.