Sensitivity of high-fidelity neural interfaces to perturbations

Thumbnail Image

Files

isbn9789526427782.pdf (25.29 MB)

URL

Journal Title

Journal ISSN

Volume Title

School of Electrical Engineering | Doctoral thesis (article-based) | Defence date: 2025-10-31
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.

Authors

Date

2025

Department

Sähkötekniikan ja automaation laitos
Department of Electrical Engineering and Automation

Major/Subject

Mcode

Degree programme

Language

en

Pages

66 + app. 58

Series

Aalto University publication series Doctoral Theses, 203/2025

Abstract

High-density surface electromyography (HD-sEMG) coupled with motor-unit decomposition (MUD) can decode spinal-level neural commands for intuitive human-machine interfaces, yet its fidelity deteriorates under sweat, movement, or sensor drift. This dissertation assesses the robustness of such interfaces and neural drive under physiological and technical perturbations and explores strategies to mitigate performance degradation. Physiological perturbations involved acute blood flow restriction (BFR), where its effects on MU discharge, neural drive, force tracking, and common synaptic were assessed. Technical perturbations included simulated additive white Gaussian noise (WGN), channel loss, and electrode shifts on the recorded clean. Decoding performance was evaluated using global EMG features and MU-level features with pattern recognition (linear discriminant analysis (LDA), deep neural network (DNN)) and regression methods (logistic regression (LR), artificial neural network (ANN), DNN). An algorithmic mitigation strategy, musclesynergy-guided channel clustering, was also explored. Key findings show that BFR alters MU firing, neural drive, and coherence (alpha band decreased, delta increased) while subjects largely maintained force tracking performance. For signal degradation, amplitude-based global EMG features (root mean square (RMS) and mean absolute value (MAV)) were most robust to WGN and channel loss, but electrode displacement caused the most significant performance drop for featurebased decoding. MUD algorithm was highly sensitive to sever global WGN (81% yield reduction) but resilient to localized perturbations. However, MU-driven decoding was generally more sensitive to perturbations than feature-based decoding. Synergy-guided clustering substantially increased extracted MU yield (69%) and improved kinematic decoding accuracy. In conclusion, the thesis assesses the sensitivity of HD-sEMG interfaces and highlights the importance of signal quality and robust features. It demonstrates that physiology-informed signal processing, such as synergy clustering, can enhance MU extraction and decoding reliability and enable the development of more robust neural interfaces for real-world use.

Description

Supervising professor

Vujaklija, Ivan, Prof., Aalto University, Department of Electrical Engineering and Automation, Finland

Thesis advisor

Vujaklija, Ivan, Prof., Aalto University, Department of Electrical Engineering and Automation, Finland

Keywords

deep neural network, human-machine interfacing, electromyography, noise

Other note

Parts

  • [Publication 1]: Mansour Taleshi, Franziska Bubeck, Pascal Brunner, Leonardo Gizzi, and Ivan Vujaklija. Observing Changes in Motoneuron Characteristics Following Distorted Sensorimotor Input via Blood-Flow Restriction. Journal of Applied Physiology, Volume 138, Pages 559–570, January 2025.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202504023074
    DOI: 10.1152/japplphysiol.00603.2024 View at publisher
  • [Publication 2]: Mansour Taleshi, Franziska Bubeck, Leonardo Gizzi, and Ivan Vujaklija. Effects of Blood-Flow Restriction on Motoneuron Synchronization. Frontiers in Neural Circuits, Volume 19, Article 1561684, May 2025.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202506044230
    DOI: 10.3389/fncir.2025.1561684 View at publisher
  • [Publication 3]: Mansour Taleshi, Dennis Yeung, Minh Dinh Trong, Francesco Negro, Stéphane Deny, and Ivan Vujaklija. Impact of Noise on Deep-Learning-Based Pseudo-Online Gesture Recognition with High-Density EMG. In Proceedings of the 47th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Copenhagen, Denmark, Pages TBD (in press), July 2025.
  • [Publication 4]: Mansour Taleshi, Dennis Yeung, Francesco Negro, and Ivan Vujaklija. Effects of Spatial and Signal-Imposed Noise on Motor Unit Decomposition. Preprint, 2025.
  • [Publication 5]: Mansour Taleshi, Dennis Yeung, Francesco Negro, and Ivan Vujaklija. Sensitivity of Non-Invasive Motor-Unit-Based Gesture Recognition to Signal Degradation. Preprint, 2025.
  • [Publication 6]: Mansour Taleshi, Dennis Yeung, Francesco Negro, and Ivan Vujaklija. Muscle Synergy-driven Motor Unit Clustering for Human-Machine Interfacing. In Proceedings of the 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, Scotland, Pages 4147–4150, July 2022.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202302011903
    DOI: 10.1109/EMBC48229.2022.9871356 View at publisher

Citation

Permanent link to this item

https://urn.fi/URN:ISBN:978-952-64-2778-2

Collections

[diss] Sähkötekniikan korkeakoulu / ELEC