Quasi-linear ICA decomposes dynamic high-density EMG by learning a low-rank time-varying transformation to restore stationarity before applying classical linear ICA, outperforming adaptive baselines on a ground-truth benchmark.
Adaptive r eal-time identification of motor unit discharges from non-stationary high-density surface electromyographic s ignals
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Quasi-Linear ICA for Motor Unit Decomposition during Dynamic Contractions
Quasi-linear ICA decomposes dynamic high-density EMG by learning a low-rank time-varying transformation to restore stationarity before applying classical linear ICA, outperforming adaptive baselines on a ground-truth benchmark.