Distributed signal processing algorithm design for neuro-sensor networks

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). Such chronic systems must satisfy challenging energy and miniaturization constraints, leading to modular designs in which multiple networked miniature neuro-sensor modules form a ‘neuro-sensor network’ (NSN). However, current multi-channel neural signal processing (NSP) algorithms were designed for traditional neuro-sensor arrays with central access to all channels. These algorithms are not suited for NSNs, as they require unrealistic bandwidth budgets to centralize the data, yet a joint neural data analysis across NSN modules is crucial. In this project, we will design adaptive multi-channel neural signal processing algorithms for BCI applications, amenable to low-power distributed or parallelizable architectures with constrained energy resources as envisaged in the NSN concept.