Electrode-Level Low-Dimensionality Does Not Guarantee Sensor Redundancy: Dual-Dataset, Participant-Grouped Validation of Parsimonious Myoelectric Gesture Decoding

Electrode-level compressibility may not imply transferable hardware redundancy in biomimetic myoelectric interfaces. This study tested whether sensor-count sufficiency discovered by trial-level analysis survives participant-grouped evaluation. Dataset A comprised 398 archived Myo Armband trials from eight gestures. Dataset B contained 864 one-second trials from 36 participants and six gestures. A timestamp audit identified extensive repeated channel values; Dataset B was therefore analyzed on a conservative 100 Hz grid with 20–45 Hz filtering. Sensor subsets and RBF-SVM parameters were selected exclusively within grouped training data using repeated nested validation. Electrode-level NMF, all 28 fixed six-sensor layouts, cyclic re-indexing, channel-block ablation, participant-cluster bootstrap, PCA, and time-domain-only sensitivity analyses were evaluated. Dataset A yielded 97.74% accuracy with six sensors and 97.93% with eight. In Dataset B, accuracy was 75.96% ± 7.47% with six sensors and 77.93% ± 6.49% with eight; the paired difference was −1.97 percentage points (corrected 95% CI, −5.50 to 1.56). The participant-cluster bootstrap interval was −3.70 to −0.31 points. Active-gesture accuracy was 71.67% and 74.35%, respectively. All fixed six-sensor layouts averaged 74.59%. Three NMF components reconstructed 89.95% ± 1.78% of held-out-participant normalized RMS patterns, with no nonconverged folds. One-position cyclic re-indexing reduced accuracy to 40.28%; channel-block ablation caused losses of 0.62–6.71 points. Low-dimensional electrode-level RMS structure did not establish removable sensors across unseen users. Compact biomimetic interfaces require registration, adaptation, or equivariant processing before physical sensor reduction.