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A data-driven EEG-EMG pair selection framework is proposed, in which channel pair selection is formulated as a constrained bi-objective optimisation problem and achieves an average classification accuracy of 89.6%, demonstrating its effectiveness in capturing physiologically meaningful corticomuscular interactions and...
It is indicated that visual fixation enhances neural response stability, while integrating eye-tracking with MI enables the development of reliable, scalable multi-command BCI systems suitable for real-world applications.
A hybrid EEG decoding framework that jointly models continuous and tokenised representations, enabling capture of both fine-grained neural structure and long-range temporal dependencies for real-time EEG-based force decoding in assistive robotics, neuro-rehabilitation, and human-machine interaction is proposed.
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