Open Positions

The primary objective of this master’s thesis is to design and develop a robotic tool capable of precisely emplacing multiple fine wire electrodes, with accurate control over the position and depth of insertion. This project aims to enhance the accuracy and efficiency of electrode placement in biomedical applications.

Goal:

Planning, designing and conducting a validation study of a custom-built dynamometer designed to measure isometric index finger forces in different force directions. The validation study will evaluate the reliability of the dynamometer across recording sessions with regard to the maximum force, speed of contraction, amplitude of muscle activity (EMG) and discharge rate of motor units. The full extent of the project can be defined in detail depending on the type of thesis/project.

Tasks:

• Conceptualize and design a robotic tool for precise electrode insertion, using CAD software to create detailed blueprints.
• Build a functional prototype with appropriate materials, incorporating actuators, sensors, and insertion mechanisms.
• Develop and implement control systems and algorithms for precise, automated electrode insertion, with real-time sensor feedback.
• Test and calibrate the prototype to ensure accuracy, reliability, and performance; optimize design and control based on analysis.
• Ensure safety and biocompatibility, conduct risk assessments, and collaborate effectively with advisors and team members

Requirements:

• Preferred study programs: Medical Engineering, Mechanical Engineering or other comparable study programs
• Proficiency in CAD software, experience with robotics and control systems, and familiarity with prototyping and testing.
• Knowledge of programming languages commonly used in robotics (e.g., Python, C++) and experience with control algorithms.
• Strong analytical and problem-solving skills, excellent written and verbal communication skills

Supervisor:

Devon Rohlf, M. Sc.

Application:

Please apply with a cover letter, CV and transcripts to nsquared@fau.de.

You will be analysing an already existing dataset of EMG signals from the first dorsal interosseus muscle (index finger abductor) and flexor digitorum muscle (index finger flexor) recorded with state-of-the-art High-Density intramuscular EMG electrodes during isometric contractions of the index finger in different force directions.

You extract the motor neuron activation patterns from the EMG signals using decomposition algorithms and investigate the flexibility of the motor commands that the motor cortex sends to the motor neurons during contractions in different force directions.

In literature, these activation patterns are often referred to as “motor unit synergies” and are an important aspect of ground-level neurophysiological research that we can now investigate with high-tech technology.

Requirements:

• Solid Matlab / Python Knowledge
• Experience in biomedical (in particular EMG) time-series signal processing
• Base knowledge in statistical methods
• Interest in robust research and scientific methods
• Preferred study programs: Medical Engineering, Computational Engineering, Data Science or any other comparable study program
• Ability to work independently and competent time management skills

Supervisor:

Marius Oßwald, M. Sc.

Application:

Please apply with a cover letter, CV and transcripts to nsquared@fau.de.

As part of an international research collaboration, you will acquire an extensive dataset of High-Density EMG signals from extrinsic hand muscles and concurrent video recordings during a variety of grasping motions. You will further analyze the acquired EMG signals and Kinematics data.

The exact type of analysis will depend on the type of thesis / research lab and can be adapted towards your personal interests, skillset and background. Multiple research lines are possible, including high-level analysis of motor unit activation patterns, factor analysis and muscle synergy analysis on the level of the EMG signals, or machine learning / deep learning towards the prediction of the exact hand and digit movements from the acquired EMG signals.

Prior base knowledge of Neuromuscular Physiology is preferred.

Requirements:

• Basic Matlab / Python Knowledge
• Experience in biomedical (in particular EMG) time-series signal processing
• Interest in robust research and scientific methods
• Preferred study programs: Medical Engineering, Computational Engineering, Data Science or any other comparable study program
• Ability to work independently and competent time management skills

Supervisor:

Marius Oßwald, M. Sc.

Application:

Please apply with a cover letter, CV and transcripts to nsquared@fau.de.

Goals:

Using High-Density surface EMG, the firing activity of individual motor units can be extracted with blind-source-separation algorithms during isometric muscle contractions. However, it is challenging to investigate the activity of the same motor unit across multiple recording sessions. Different approaches for the tracking of identical motor units across recordings exist. In this study, we want to validate and compare different algorithms deisgned to identify identical motor untits across sessions with concurrent recordings of intramuscular EMG (ground truth) and HD-sEMG signals. Prior base knowledge of Neuromuscular Physiology regarding EMG and Motor Unit theory and analysis is preferred.

Requirements:

• Solid Matlab / Python Knowledge
• Experience in biomedical (in particular EMG) signal processing
• Base knowledge in statistical methods
• Interest in robust research and scientific methods

Supervisor:

Marius Oßwald, M. Sc.

Application:

Please apply with a cover letter, CV and transcripts to nsquared@fau.de.

Electromyography (EMG) is a measurement method used to assess muscle activity and activation through sensors placed on the skin. Modern algorithms leverage high-density EMG (HDsEMG) arrays to capture signals from muscles beneath the skin surface, enabling, e.g., gesture recognition in individuals with paralysis. The integration of HD-sEMG into textiles using printed electronics marks a significant innovation, facilitating accurate and rapid data acquisition for patients with motor impairments after neuromuscular lesions.

This project is conducted in collaboration with the Noxon GmbH in Munich.

Goals:

Requirements:

• Solid Matlab and Python Knowledge
• Experience in biomedical signal processing
• Experience in electrical design and CAD

Supervisor:

Dominik Braun, M. Sc.

Application:

Please apply with a cover letter, CV and transcripts to nsquared@fau.de.

An important component for revealing the neurophysiology of human movement is the specific connections of our sensorimotor system. Therefore, especially peripheral nerve stimulation is a technique to elicit reflex responses in muscles, revealing the direct connections from sensory to motor neurons. Our lab constructed a self-made pneumatically driven “tendon tapper”, which elicits reflex responses in the same way as a medical doctor is doing using a reflex hammer to elicit those. The key advantage of the tendon tapper is its ability to operate automatically leading to reproducible measurements.

Goal:

The aim of the proposed thesis is the design and validation of a test setup to evoke various reflex responses using the tendon tapper. During the development process, it should be focused on a dynamic adaptation of the system to the human body, enabling the assessment of reflexes over different body regions. Furthermore, the reflex responses should be validated by comparing the stability of the electromyographic (EMG) response of the muscles to the ones elicited by conventional reflex hammers.

Requirements:

• Preferred study programs: Medical Engineering, Computational Engineering, Data Science or any other comparable study program
• Proficient Programming skills (preferably Python and MATLAB)
• Basic CAD knowledge
• Basic micro controller knowledge
• Ability to work independently and competent time management skills

Supervisor:

Yannick Finck, M.Sc. and Finja Beermann, M.Sc.

Application:

Please apply with a cover letter, CV and transcripts to nsquared@fau.de.

As part of an international research collaboration, you will acquire an extensive dataset of High-Density EMG signals from extrinsic hand muscles and concurrent video recordings during a variety of grasping motions. You will further analyze the acquired EMG signals and Kinematics data.

The exact type of analysis will depend on the type of thesis / research lab and can be adapted towards your personal interests, skillset and background. Multiple research lines are possible, including high-level analysis of motor unit activation patterns, factor analysis and muscle synergy analysis on the level of the EMG signals, or machine learning / deep learning towards the prediction of the exact hand and digit movements from the acquired EMG signals.

Prior base knowledge of Neuromuscular Physiology is preferred.

Requirements:

• Basic Matlab / Python Knowledge
• Experience in biomedical (in particular EMG) time-series signal processing
• Interest in robust research and scientific methods
• Preferred study programs: Medical Engineering, Computational Engineering, Data Science or any other comparable study program
• Ability to work independently and competent time management skills

Supervisor:

Marius Oßwald, M. Sc.

Application:

Please apply with a cover letter, CV and transcripts to nsquared@fau.de.

As part of an international research collaboration, you will acquire an extensive dataset of High-Density EMG signals from extrinsic hand muscles and concurrent video recordings during a variety of grasping motions. You will further analyze the acquired EMG signals and Kinematics data.

The exact type of analysis will depend on the type of thesis / research lab and can be adapted towards your personal interests, skillset and background. Multiple research lines are possible, including high-level analysis of motor unit activation patterns, factor analysis and muscle synergy analysis on the level of the EMG signals, or machine learning / deep learning towards the prediction of the exact hand and digit movements from the acquired EMG signals.

Prior base knowledge of Neuromuscular Physiology is preferred.

Requirements:

• Basic Matlab / Python Knowledge
• Experience in biomedical (in particular EMG) time-series signal processing
• Interest in robust research and scientific methods
• Preferred study programs: Medical Engineering, Computational Engineering, Data Science or any other comparable study program
• Ability to work independently and competent time management skills

Supervisor:

Marius Oßwald, M. Sc.

Application:

Please apply with a cover letter, CV and transcripts to nsquared@fau.de.

An important component for revealing the neurophysiology of human movement is the specific connections of our sensorimotor system. Therefore, especially peripheral nerve stimulation is a technique to elicit reflex responses in muscles, revealing the direct connections from sensory to motor neurons. Our lab constructed a self-made pneumatically driven “tendon tapper”, which elicits reflex responses in the same way as a medical doctor is doing using a reflex hammer to elicit those. The key advantage of the tendon tapper is its ability to operate automatically leading to reproducible measurements.

Goal:

The aim of the proposed thesis is the design and validation of a test setup to evoke various reflex responses using the tendon tapper. During the development process, it should be focused on a dynamic adaptation of the system to the human body, enabling the assessment of reflexes over different body regions. Furthermore, the reflex responses should be validated by comparing the stability of the electromyographic (EMG) response of the muscles to the ones elicited by conventional reflex hammers.

Requirements:

• Preferred study programs: Medical Engineering, Computational Engineering, Data Science or any other comparable study program
• Proficient Programming skills (preferably Python and MATLAB)
• Basic CAD knowledge
• Basic micro controller knowledge
• Ability to work independently and competent time management skills

Supervisor:

Yannick Finck, M.Sc. and Finja Beermann, M.Sc.

Application:

Please apply with a cover letter, CV and transcripts to nsquared@fau.de.