EEG-based Brain Signatures for Personalized Therapeutic Intervention in Chronic Pain

Chronic-pain management remains a complex clinical challenge, often requiring personalized therapeutic approaches. EEG data offers valuable insights into the underlying brain activity associated with chronic pain conditions. Traditional analysis methods may overlook crucial information, necessitating a focus on constructing forward models based on EEG-derived brain signatures. These models can facilitate personalized interventions by targeting specific brain regions implicated in pain processing.

This thesis aims at developing advanced techniques for constructing forward models based on EEG-derived brain signatures in chronic pain patients. Machine learning (ML) and data-driven approaches will be utilized to identify and characterize unique brain signatures associated with different chronic pain conditions, including neuropathic and fibromyalgia pain. This will be the first key-step towards personalized therapeutic interventions through in-silico stimulation and computational modeling, with the ultimate goal of optimizing treatment strategies for individual patients.

This project is in collaboration with Dr. Christos Strydis (c.strydis@tudelft.nl; Erasmus MC and TU Delft) and Prof. Dr. Dirk De Ridder (University of Otago, New Zealand).

Requirements

Signal processing, machine learning, Python

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Last modified: 2024-02-26