There is growing evidence that in people with neurodegenerative diseases, like dementia and Parkinson, wide-spread changes in functional brain connectivity occur much earlier than clinically relevant symptoms of the disease. A key requirement in the development of early predictive models as well as novel interventions targeting synaptic and neuronal integrity is the ability to reliably measure brain function in a natural environment during spontaneous behaviour. Existing advanced neuroimaging techniques are only available in a clinical setting, while existing wearable systems do not offer sufficient spatial coverage and resolution for network analysis and suffer from poor signal quality and significant motion artefacts. Additionally, there is interest from the neuroscientific community to investigate novel electrical brain stimulation approaches which may induce neuroplasticity in the brain. This requires novel electrical stimulation circuits that can provide stimulation deeper into the brain and with higher spatial resolution.
This PhD aims to develop a wireless and easy-to-operate high-density (HD)-EEG headset (>64channels) suitable for use in an ambulatory setting under natural behaviour. To improve the reliability and signal quality compared to existing dry-electrode systems, one of the approaches to be investigated is to embed active amplifier chips in each electrode to locally amplify and digitize the EEG signal. Dry electrodes are typically built from conducting polymers with multiple finger-like structures that easily penetrate the hair. By extracting signals from individual fingers, it might be possible to improve spatial resolution and signal robustness. By recording from individual fingers in a single electrode and using signal quality assessment algorithms, we will be able to disable parts of the electrode that do not contribute to actual useful signal or find a combination that results in optimal signal quality. The candidate will also include novel stimulation circuitry. Multi-electrode high-frequency stimulation circuits including steering logic could potentially lead to more focused and deeper stimulation.
The candidate for this PhD should have a very strong knowledge on electronic system design using commercial-off-the-shelf (COTS) components. The core circuits will be analog in nature, including instrumentation amplifiers, filters, current generators, ... However basic knowledge of microcontroller/FGPA and standard digital IO protocols is needed also. During the course of this PhD, the candidate will be tasked to come up with novel circuit ideas and design, built and test the circuits in proof of concepts demonstrator PCBs. In a later phase, the candidate will need to scale these up in build a complete wireless EEG headset suitable for initial human trials.
- PCB design schematic + layout
- Analog circuit design (amplifiers, filters, current generators, ...)
- Basic knowledge of mathematical tools like Matlab / Python / ...
Required background: Electrical engineering
Type of work: 60% electrical design (PCB-level), 30% testing (lab), 10% pre-clinical testing/validation
Supervisor: Chris Van Hoof
Daily advisor: Nick Van Helleputte
The reference code for this position is 2020-103. Mention this reference code on your application form.
Chinese nationals who wish to apply for the CSC scholarship, should use the following code when applying for this topic: CSC2020-52.