Adaptive HD-sEMG decomposition: towards robust real-time decoding of neural drive
In: Journal of Neural Engineering, Jg. 21 (2024), Heft 2, S. 026012
academicJournal
Zugriff:
Objective . Neural interfacing via decomposition of high-density surface electromyography (HD-sEMG) should be robust to signal non-stationarities incurred by changes in joint pose and contraction intensity. Approach . We present an adaptive real-time motor unit decoding algorithm and test it on HD-sEMG collected from the extensor carpi radialis brevis during isometric contractions over a range of wrist angles and contraction intensities. The performance of the algorithm was verified using high-confidence benchmark decompositions derived from concurrently recorded intramuscular electromyography. Main results . In trials where contraction conditions between the initialization and testing data differed, the adaptive decoding algorithm maintained significantly higher decoding accuracies when compared to static decoding methods. Significance . Using “gold standard” verification techniques, we demonstrate the limitations of filter re-use decoding methods and show the necessity of parameter adaptation to achieve robust neural decoding.
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Adaptive HD-sEMG decomposition: towards robust real-time decoding of neural drive
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Autor/in / Beteiligte Person: | Yeung, Dennis ; Negro, Francesco ; Vujaklija, Ivan ; European Research Council ; Academy of Finland |
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Zeitschrift: | Journal of Neural Engineering, Jg. 21 (2024), Heft 2, S. 026012 |
Veröffentlichung: | IOP Publishing, 2024 |
Medientyp: | academicJournal |
ISSN: | 1741-2560 |
DOI: | 10.1088/1741-2552/ad33b0 |
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