Signal Processing Algorithm Design for Next-Generation Neuro-Sensor Technology
The possibility to chronically monitor the brain 24/7 in daily-life activities would revolutionize human-machine interactions through the use of brain-computer interfaces (BCI) and would allow for ambulant monitoring of patients with neurological disorders. Such chronic systems must satisfy challenging energy and miniaturization constraints, leading to modular designs in which multiple networked miniature neuro-sensor modules are interconnected. This comes with several challenges such as low-SNR signals, a reduced and more localized spatial correlation of the signals across the different sensor nodes, and limited battery capacity. In this project, we will design adaptive data-driven neural signal processing algorithms that can be embedded in such low-power and miniaturized neuro-sensor platforms with distributed or parallelizable architectures, in the context of various BCI and biomedical applications.