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This paper proposes a multi-subject pretraining approach for surface EMG speech decoding that reduces calibration time while improving accuracy, achieving a character error rate of 21.7% with just three minutes of target-subject data.
This paper introduces a montage-agnostic encoder for surface-EMG gesture decoding that maintains recognition accuracy across recording sessions without recalibration, and shows that feature-statistic alignment at test time improves adaptation on NinaPro DB6.