A new open dataset and benchmark show that wrist sEMG can classify finger force intentions during isometric (no-movement) contractions, with about 93 percent single-day accuracy and lower cross-day and cross-subject accuracy.
Neural decoding of imagined speech and visual imagery as intuitive paradigms for BCI communication,
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sEMG-based Gesture-Free Hand Intention Recognition: System, Dataset, Toolbox, and Benchmark Results
A new open dataset and benchmark show that wrist sEMG can classify finger force intentions during isometric (no-movement) contractions, with about 93 percent single-day accuracy and lower cross-day and cross-subject accuracy.