PhD on Robust decomposition of High-Density Surface EMG Signals

Updated: 7 months ago
Deadline: 21 Nov 2021


Biomedical Diagnostic (BM/d) Lab

at the Eindhoven University of Technology (TU/e) is seeking an outstanding PhD candidate to work in the field of electrophysiological signal processing, within a collaboration with the

Neuro-Mechanical Modeling & Engineering Lab

at the University of Twente.

Project description

High-density surface electromyography (HD-sEMG) is a technique to study the muscular electrical activity by using 2-dimensional grids with a multitude of closely spaced electrodes. In recent years, it has been used in various disciplines like clinical neurophysiology, kinesiology, sport science, neurorehabilitation, and wearable robotics (prostheses and exoskeletons).

HD-sEMG can be applied at different levels. At cellular-level, HD-sEMG has been used to unravel the interplay between the nervous system and the musculoskeletal system by identifying the single motor unit activity, i.e. muscles' basic contractile unit. Information about single motor unit activity open up new avenues for man-machine interfacing (i.e. intuitive control of bionic limbs) as well as for the advanced diagnosis of neuro-motor function in healthy and impaired individuals. Various techniques to decompose HD-sEMG into motor unit discharge patterns have been developed in the last decades. Their applications are currently however limited to lab-based measurements, where high-quality signals acquired during low-level isometric contraction are processed.

Recent applications of HD-sEMG at motor system level have also been proposed. A crucial step for out-of-the-lab applications is to use HD-sEMG to cluster electromyographic activity into muscle groups. The automatic identifications of muscle groups based on HD-sEMG recordings is based on dynamic maps that provide information on the spatial distribution of electrical potentials and their activation sequence in relation to different movements. Possible applications relate to prosthetic or exoskeleton control. However, the robustness and flexibility of current signal processing techniques to different conditions is still insufficient, hampering the uptake of this technology in real-life scenarios.

Novel signal processing strategies for accurate and real-time decomposition of HD-sEMG acquired in the challenging ecological scenario (i.e., high-level dynamic contraction with poor signal quality) are needed to extend the potential applications to clinical practice.

During your PhD, you will work on the conceptualization and development of decomposition strategies for HD-sEMG. To this end, a probabilistic framework and 'informed' blind source separation algorithms will be developed that account for the full measurement chain, including the characterization of signal and interference sources, noise statistics, and major artifacts. This will be reinforced by active involvement in HD-sEMG recording, both in healthy and pathological individuals. Together with a team of engineers and clinicians, you will aid the clinical translation of the developed solutions.

Your task will be:

  • Investigate the current state-of-the-art algorithms to decompose the EMG in primitive signals at both single motor unit level and motor system level.
  • Develop a novel signal processing strategy able to extract information from HD-sEMG in real-time, dealing with dynamic tasks, artifacts and low-quality signals typical of real-life acquisition.
  • Record HD-sEMG from human leg muscles in healthy and pathological conditions (e.g. stroke, spinal cord injury patients), in collaboration with the University of Twente.
  • Validate the algorithm proposed and compare its effectiveness with state-of-the-art algorithms under different working conditions (e.g., high-level contraction, dynamic muscular condition).

Academic and Research Environment:

Eindhoven University of Technology (TU/e) is one of Europe's top technological universities, situated in the heart of one of Europe's largest high-tech innovation ecosystems. Research at TU/e is characterized by a combination of academic excellence and a strong real-world impact. This impact is pursued via close collaboration with high-tech industries and clinical partners.

Research related to this position will be carried out at the Biomedical Diagnostics (BM/d) lab of the Signal Processing Systems (SPS) group, which is part of the Electrical Engineering department. The BM/d lab, chaired by Prof. Mischi, has a strong track record in electrophysiological signal processing, physiological modelling and quantitative analysis of biosignals, ranging from ultrasound and MRI to electrophysiology. For more information, see .

The candidate will have the opportunity to work with various members of the SPS group and will be tightly collaborating with the Neuro-Mechanical Modeling & Engineering Lab at the University of Twente.

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