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PostDoc in computer science: Adaptive deep brain stimulation


Deep brain stimulation (DBS) is an established therapy for Parkinson’s disease (PD) and essential tremor and is under research also for, e.g., chronic pain, major depression disorder and anxiety disorders. Stimulation parameters (for example, amplitude or frequency) are typically determined by hand by an expert clinician. This infrequent tuning has been found insufficient to adapt to fast changes in the disease progress or varying symptoms intensity due to e.g. medication or external factors.

These challenges are addressed by research on closed-loop DBS systems that allow to adapt stimulation parameters in real time as a function of both behavioral metrics and brain signals such as electroencephalogram, electrocorticogram and local field potentials. An important prerequisite for closed-loop control of DBS is the robust decoding of neural markers which provide information about the current state of the patient and which can serve as features to inform a controller. As neural markers and brain signals in general are very subject specific, machine learning methods play a central role for this decoding task. The research background of our group in the field of brain-computer interfaces provides us with both, experimental paradigms and decoding methods in order to investigate the building blocks of DBS-based closed-loop systems.

We conduct this research in collaboration between the Brain State Decoding Lab (PI Tangermann, University of Freiburg) and the Dept. Stereotactic and Functional Neurosurgery (PI Coenen, University Medical Center Freiburg) and companies focussing on neural implant technology.


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