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Adaptive EMG decomposition in dynamic conditions based on online learning metrics with tunable hyperparameters.
Mendez Guerra, Irene; Barsakcioglu, Deren Y; Farina, Dario.
Affiliation
  • Mendez Guerra I; Department of Bioengineering, Imperial College London, London, United Kingdom.
  • Barsakcioglu DY; Department of Bioengineering, Imperial College London, London, United Kingdom.
  • Farina D; Department of Bioengineering, Imperial College London, London, United Kingdom.
J Neural Eng ; 21(4)2024 Jul 29.
Article in En | MEDLINE | ID: mdl-38959878
ABSTRACT
Objective. Developing neural decoders robust to non-stationary conditions is essential to ensure their long-term accuracy and stability. This is particularly important when decoding the neural drive to muscles during dynamic contractions, which pose significant challenges for stationary decoders.Approach. We propose a novel adaptive electromyography (EMG) decomposition algorithm that builds on blind source separation methods by leveraging the Kullback-Leibler divergence and kurtosis of the signals as metrics for online learning. The proposed approach provides a theoretical framework to tune the adaptation hyperparameters and compensate for non-stationarities in the mixing matrix, such as due to dynamic contractions, and to identify the underlying motor neuron (MN) discharges. The adaptation is performed in real-time (∼22 ms of computational time per 100 ms batches).Main results. The hyperparameters of the proposed adaptation captured anatomical differences between recording locations (forearm vs wrist) and generalised across subjects. Once optimised, the proposed adaptation algorithm significantly improved all decomposition performance metrics with respect to the absence of adaptation in a wide range of motion of the wrist (80∘). The rate of agreement, sensitivity, and precision were⩾90%in⩾80%of the cases in both simulated and experimentally recorded data, according to a two-source validation approach.Significance. The findings demonstrate the suitability of the proposed online learning metrics and hyperparameter optimisation to compensate the induced modulation and accurately decode MN discharges in dynamic conditions. Moreover, the study proposes an experimental validation method for EMG decomposition in dynamic tasks.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Electromyography Limits: Adult / Female / Humans / Male Language: En Journal: J Neural Eng Journal subject: NEUROLOGIA Year: 2024 Document type: Article Affiliation country: Reino Unido Country of publication: Reino Unido

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Electromyography Limits: Adult / Female / Humans / Male Language: En Journal: J Neural Eng Journal subject: NEUROLOGIA Year: 2024 Document type: Article Affiliation country: Reino Unido Country of publication: Reino Unido