HD-EMG to Assess Motor Learning in Myoelectric Control

Sigrid S.G. Dupan; Ivan Vujaklija; Giulia De Vitis; Strahinja S. Dosen; Dario Farina; Dick F. Stegeman

doi:10.1007/978-3-030-01845-0_226

HD-EMG to Assess Motor Learning in Myoelectric Control

Sigrid S.G. Dupan^*, Ivan Vujaklija, Giulia De Vitis, Strahinja S. Dosen, Dario Farina, Dick F. Stegeman

^*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

Abstract

Online myoelectric control involves two types of adaptation: computational adaptation, in which the controller learns to associate muscle patterns with performed forces; and behavioural adaptation, where the users learn the new interface, and adapt their motor control strategies based on the errors they observe. In order to study the behavioural motor learning during online myoelectric control, twelve able-bodied participants performed single and 2-finger presses through force and myoelectric control. Myoelectric control was obtained with linear ridge regression, and was based on a training set only containing single finger presses. The distance between muscle patterns of force and EMG control trials indicated that motor learning leads to changes in neural drive, even on the trained presses. This suggests that motor learning is an integral part of myoelectric control, where the ability of the user to learn the EMG-to-force mapping impacts the overall performance of the myoelectric controller.

Original language	English
Title of host publication	Proceedings of the 4th International Conference on NeuroRehabilitation : ICNR2018
Editors	Lorenzo Masia, Silvestro Micera, Metin Akay, José L. Pons
Number of pages	5
Publisher	Springer Publishing Company
Publication date	1 Jan 2019
Pages	1131-1135
ISBN (Print)	978-3-030-01844-3
ISBN (Electronic)	978-3-030-01845-0
DOIs	https://doi.org/10.1007/978-3-030-01845-0_226
Publication status	Published - 1 Jan 2019
Event	International Conference on Neurorehabilitation - Pisa, Italy Duration: 16 Oct 2018 → 20 Oct 2018 http://www.icnr2018.org

Conference

Conference	International Conference on Neurorehabilitation
Country/Territory	Italy
City	Pisa
Period	16/10/2018 → 20/10/2018
Internet address	http://www.icnr2018.org

Series	Biosystems and Biorobotics
Volume	21
ISSN	2195-3562

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10.1007/978-3-030-01845-0_226

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Dupan, S. S. G., Vujaklija, I., De Vitis, G., Dosen, S. S., Farina, D., & Stegeman, D. F. (2019). HD-EMG to Assess Motor Learning in Myoelectric Control. In L. Masia, S. Micera, M. Akay, & J. L. Pons (Eds.), Proceedings of the 4th International Conference on NeuroRehabilitation: ICNR2018 (pp. 1131-1135). Springer Publishing Company. Biosystems and Biorobotics Vol. 21 https://doi.org/10.1007/978-3-030-01845-0_226

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title = "HD-EMG to Assess Motor Learning in Myoelectric Control",

abstract = "Online myoelectric control involves two types of adaptation: computational adaptation, in which the controller learns to associate muscle patterns with performed forces; and behavioural adaptation, where the users learn the new interface, and adapt their motor control strategies based on the errors they observe. In order to study the behavioural motor learning during online myoelectric control, twelve able-bodied participants performed single and 2-finger presses through force and myoelectric control. Myoelectric control was obtained with linear ridge regression, and was based on a training set only containing single finger presses. The distance between muscle patterns of force and EMG control trials indicated that motor learning leads to changes in neural drive, even on the trained presses. This suggests that motor learning is an integral part of myoelectric control, where the ability of the user to learn the EMG-to-force mapping impacts the overall performance of the myoelectric controller.",

author = "Dupan, {Sigrid S.G.} and Ivan Vujaklija and {De Vitis}, Giulia and Dosen, {Strahinja S.} and Dario Farina and Stegeman, {Dick F.}",

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booktitle = "Proceedings of the 4th International Conference on NeuroRehabilitation",

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note = "International Conference on Neurorehabilitation, ICNR ; Conference date: 16-10-2018 Through 20-10-2018",

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Dupan, SSG, Vujaklija, I, De Vitis, G, Dosen, SS, Farina, D & Stegeman, DF 2019, HD-EMG to Assess Motor Learning in Myoelectric Control. in L Masia, S Micera, M Akay & JL Pons (eds), Proceedings of the 4th International Conference on NeuroRehabilitation: ICNR2018. Springer Publishing Company, Biosystems and Biorobotics, vol. 21, pp. 1131-1135, International Conference on Neurorehabilitation, Pisa, Italy, 16/10/2018. https://doi.org/10.1007/978-3-030-01845-0_226

HD-EMG to Assess Motor Learning in Myoelectric Control. / Dupan, Sigrid S.G.; Vujaklija, Ivan; De Vitis, Giulia et al.

Proceedings of the 4th International Conference on NeuroRehabilitation: ICNR2018. ed. / Lorenzo Masia; Silvestro Micera; Metin Akay; José L. Pons. Springer Publishing Company, 2019. p. 1131-1135 (Biosystems and Biorobotics, Vol. 21).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - HD-EMG to Assess Motor Learning in Myoelectric Control

AU - Dupan, Sigrid S.G.

AU - Vujaklija, Ivan

AU - De Vitis, Giulia

AU - Dosen, Strahinja S.

AU - Farina, Dario

AU - Stegeman, Dick F.

PY - 2019/1/1

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N2 - Online myoelectric control involves two types of adaptation: computational adaptation, in which the controller learns to associate muscle patterns with performed forces; and behavioural adaptation, where the users learn the new interface, and adapt their motor control strategies based on the errors they observe. In order to study the behavioural motor learning during online myoelectric control, twelve able-bodied participants performed single and 2-finger presses through force and myoelectric control. Myoelectric control was obtained with linear ridge regression, and was based on a training set only containing single finger presses. The distance between muscle patterns of force and EMG control trials indicated that motor learning leads to changes in neural drive, even on the trained presses. This suggests that motor learning is an integral part of myoelectric control, where the ability of the user to learn the EMG-to-force mapping impacts the overall performance of the myoelectric controller.

AB - Online myoelectric control involves two types of adaptation: computational adaptation, in which the controller learns to associate muscle patterns with performed forces; and behavioural adaptation, where the users learn the new interface, and adapt their motor control strategies based on the errors they observe. In order to study the behavioural motor learning during online myoelectric control, twelve able-bodied participants performed single and 2-finger presses through force and myoelectric control. Myoelectric control was obtained with linear ridge regression, and was based on a training set only containing single finger presses. The distance between muscle patterns of force and EMG control trials indicated that motor learning leads to changes in neural drive, even on the trained presses. This suggests that motor learning is an integral part of myoelectric control, where the ability of the user to learn the EMG-to-force mapping impacts the overall performance of the myoelectric controller.

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ER -

Dupan SSG, Vujaklija I, De Vitis G, Dosen SS, Farina D, Stegeman DF. HD-EMG to Assess Motor Learning in Myoelectric Control. In Masia L, Micera S, Akay M, Pons JL, editors, Proceedings of the 4th International Conference on NeuroRehabilitation: ICNR2018. Springer Publishing Company. 2019. p. 1131-1135. (Biosystems and Biorobotics, Vol. 21). doi: 10.1007/978-3-030-01845-0_226

HD-EMG to Assess Motor Learning in Myoelectric Control

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