Decoding Kinematic Performance from Brain Signals in Parkinson’s Disease
Masterthesis / Biomedical Engineering
Status: Open
Time frame: Unspecified
Decoding Kinematic Performance from Brain Signals in Parkinson’s Disease
Project background
Fine motor control is impaired in patients with Parkinson’s disease. Kinematic data derived from high-resolution digital spiral drawing can capture various metrics (velocity, acceleration, accuracy, stylus pressure etc.), which can serve to precisely validate motor impairment. Brain signals recorded from the cortical surface (EEG) and from deep brain structures (local field potential – LFP) have shown to be informative about clinical symptom states of patients with Parkinsons’ disease.
The overall goal of the project, conducted in patients with Parkinson’s disease implanted with Deep Brain Stimulation (DBS) systems, is to develop computational methods to process spiral drawing signals, extract quantitative motor features, and link them with oscillatory brain activity, while systematically assessing how this relationship is influenced by dopaminergic medication and brain stimulation.
The outcome of this project will contribute to the characterization of neurophysiological biomarkers that reflect fine motor control in Parkinson’s disease. These biomarkers are essential for developing personalized neuromodulation strategies.
Possible Thesis Directions / Tasks
The thesis content is part of a larger umbrella project and can be adapted to the student’s interest and background. Key methodological aspects will include the following:
Processing of digitalized high-resolution Spiral-Drawing Task data (MATLAB)
Extraction of temporal and spatial coordinates (X, Y) and pressure.
Signal preprocessing: noise filtering, resampling, segmentation.
Feature computation: velocity, acceleration, jerk, tremor amplitude, spiral smoothness, frequency-domain descriptors.
Error metrics: deviation from template spiral, path variability, entropy-based
measures.
Relating kinematic data to brain signals (MATLAB)
Preprocessing of neural data: resampling, artifact removal, frequency analysis.
Feature extraction: beta/gamma band power, temporal dynamics, crossfrequency coupling.
Synchronization of spiral kinematics with neural signals.
Statistical analysis and regression modeling of motor–neural relationships.
This work will be conducted at the intersection of engineering and neuroscience. Through this thesis, the student will gain hands-on experience in biomedical signal processing, multimodal data integration, and computational modeling using MATLAB and learn how to design algorithms that extract meaningful features from both behavioral (spiral drawing) and neural (EEG/LFP) data. The skills acquired are highly transferable to careers in biomedical engineering, medical technology, and applied data science. The project’s clinical context offers valuable insight into the development of next-generation healthcare and neuromodulation technologies.
Requirements
Background in biomedical engineering, electrical engineering, computer science, or applied mathematics.
Strong skills in programming (MATLAB).
Knowledge of digital signal processing (filtering, spectral analysis, time-frequency methods).
Interest in biomedical applications, neurotechnology, brain-computer interfaces and neurology
Supervisor
PD Dr. Gerd Tinkhauser, MD, PhD
Working Environment
The project will be carried out in the Neurophysiology and Adaptive Neuromodulation lab, led by PD Dr. Gerd Tinkhauser, MD, PhD at the Department of Neurology, Bern University Hospital. This is an interdisciplinary work environment combining neuroscience, engineering, neurotechnology, clinical neurology & neurosurgery;
Language: English
Contact: gerd.tinkhauser@insel.ch
Application: If you are interested in this project, please send:
A short motivation letter
Curriculum Vitae
Prior experience in signal processing or machine learning
Desired starting date and duration