Background: It has become evident only recently that most brain disorders, such as pain, depression, epilepsy etc., are not the consequence of one disease-provoking area in the brain, but rather due to emergent properties of altered network activity and connectivity. The intention of this thesis is to create a brain model based on EEG data from actual patients that can help in the development of next-generation smart neuromodulation devices. These devices will, thus, shift the paradigm from the current single-area neuromodulation to network neuromodulation.
Thesis goal:To create a brain model based on neural recordings from patients and subsequently formulate technical (hardware/software) specifications for next-generation neural implants that can achieve network neuromodulation.
Keywords: Network neuromodulation, smart neuromodulation, neurological disorders, EEG
Prerequisites: Python/MATLAB, data analytics, machine/deep learning
Optionally: GPU programming, FPGA/hardware design, Signal processing (basic)
Miscellaneous: This is a PhD position offered jointly by the Delft University of Technology (Quantum & Computer Engineering department) and the Erasmus Medical Center (Neuroscience department). It capitalizes on the Convergence between the two universities and offers dual working locations (Delft, Rotterdam), access to extended resources and a truly interdisciplinary environment for conducting research.