3 dagen geleden - Technische Universiteit Delft (TUD) - Delft
PhD on Robust decomposition of High-Density Surface EMG Signals
Are you interested in developing fundamental knowledge in signal processing to push the neurorehabilitation of the future? Do you enjoy technical challenges? This stimulating PhD position might be the right one for you!
- de Rondom, Eindhoven, Noord-Brabant
- Tijdelijk contract / Tijdelijke opdracht
- Uren per week:
- 38 uur
The 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.
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 https://www.tue.nl/en/research/research-groups/biomedical-diagnostics-lab/.
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.
We are looking for candidates that match the following profile:
- Master's degree in Computer/Electrical/Biomedical Engineering or related disciplines with excellent grades.
- Excellent knowledge of signal processing and systems.
- Proven programming skills (e.g., in Matlab, Python, C, C++).
- Team player attitude, interested in collaborating with different teams.
- High motivation and creativity
- Good communication and organization skills.
- Excellent English language skills (writing and presenting).
- Knowledge of electrophysiological signal processing
- A meaningful job in a dynamic and ambitious university with the possibility to present your work at international conferences.
- A full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months.
- To develop your teaching skills, you will spend 10% of your employment on teaching tasks.
- To support you during your PhD and to prepare you for the rest of your career, you will make a Training and Supervision plan and you will have free access to a personal development program for PhD students (PROOF program).
- A gross monthly salary and benefits (such as a pension scheme, pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labor Agreement for Dutch Universities.
- Additionally, an annual holiday allowance of 8% of the yearly salary, plus a year-end allowance of 8.3% of the annual salary.
- Should you come from abroad and comply with certain conditions, you can make use of the so-called '30% facility', which permits you not to pay tax on 30% of your salary.
- A broad package of fringe benefits, including an excellent technical infrastructure, moving expenses, and savings schemes.
- Family-friendly initiatives are in place, such as an international spouse program, and excellent on-campus children day care and sports facilities.
Additional informationMore information
Do you recognize yourself in this profile and would you like to know more? Please contact
prof. M. Mischi (m.mischi[at]tue.nl) or dr. E. Peri (e.peri[at]tue.nl) for more information about the project and any informal enquiries.
For information about terms of employment, click here or contact hrservices.flux[at]tue.nl.
Please visit www.tue.nl/jobs to find out more about working at TU/e!
We invite you to submit a complete application by using the 'apply now'-button on this page.
The application should include a:
- a cover letter explaining your motivation and suitability for the position;
- a detailed Curriculum Vitae (including English proficiency level, nationality, VISA requirements, date of birth as well as programming skills, experience overview, publication list (if any), and key achievements in research project(s));
- contact information of two academic references;
- copies of diplomas with course grades;
Candidates will be selected based on graduation mark and proficiency at university including consideration of the reputation of the university, relevant experience and skills, writing skills and publications, work experience as well as performance in interviews.
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