Decoding of hand force from EEG recordings


For the rehabilitation training after stroke, simple repetitive hand motor tasks have been developed. One of them, the Sequential Visual Isometric Pinch Task (SVIPT) has been performed in studies with healthy users and stroke patients while the user’s brain activity (electroencephalogram, EEG) has been recorded. In addition, a continuous version of the SVIPT has been used in a study with healthy users. In our research, we found that multiple aspects of the behavioral performance in these tasks is reflected in the EEG recordings.

Research question

In this project, it shall be investigated, if information about the force applied by the user’s hand can be decoded from the EEG signal using machine learning.

The candidate will perform an analysis of existing data from healthy users and stroke patients in order to investigate the research question. The candidate can built upon existing data analysis pipelines in Python. A background on deep learning with convolutional neural networks is required.

Skills required:

  • Machine learning
  • Good programming skills in Python (familiarity with numpy, sklearn, at least one popular deep learning framework).
  • Good mathematical background and intuition.
Michael Tangermann
Michael Tangermann
Head of the Lab, Associate Professor, PI