PhD - Leuven | More than two weeks ago
Brain stents, also known as intracranial stents or neurovascular stents, are medical devices used in the treatment of certain neurological conditions, particularly those related to blood vessel abnormalities in the brain. These stents are designed to be placed inside blood vessels in the brain to provide support and improve blood flow. Recently, brain stents equipped with arrays of electrodes (i.e. stentrodes) have been introduced and demonstrated as safe alternatives to placing brain-computer-interface (BCI) electrodes in or on the dura by open-brain surgery. With such devices, it is possible to record neural signals from a blood vessel, enabling the control of external devices using just thoughts.
The inherent minimally-invasive implantation technique employed in this endovascular approach renders it an attractive choice for BCIs. However, to ensure a robust decoding or classification accuracy, it is imperative to enhance the quality of recorded signals. The focal point of this PhD research centers on conceiving an active brain stent aimed at enhancing the signal-to-noise ratio in comparison to conventional stentrodes. This innovation will be realized through the incorporation of low-noise readout electronics directly into the electrode nodes. Moreover, these electrode nodes will also feature electrical stimulation capabilities, thereby giving rise to a bidirectional distributed neural interface.
The main goal of this PhD topic is to investigate and design low-noise and low-power neural recording and stimulation CMOS circuits that work and communicate in a distributed fashion. The fabricated sensor nodes will be latter integrated in an active stent prototype. The candidate will contribute to the electrical characterization of these prototype systems in real biological applications.
Skills and background:
Required background: Electrical or electronics engineering
Type of work: 40% IC design, 30% system design, 20% experimental, 10% literature
Supervisor: Georges Gielen
Co-supervisor: Carolina Mora Lopez
Daily advisor: Carolina Mora Lopez
The reference code for this position is 2024-122. Mention this reference code on your application form.