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Distributed Signal Processing Algorithms for Chronic Neuro-Sensor Networks

Objective

The possibility to chronically monitor the brain 24/7 in daily-life activities would revolutionize human-machine interactions and health care, e.g., in the context of neuroprostheses, neurological disorders, and 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.

The central idea of this project is to remove this algorithm bottleneck by designing novel scalable, distributed NSP algorithms to let the modules of an NSN jointly process the recorded neural data through in-network data fusion and with a minimal exchange of data.

To guarantee impact, we mainly focus on establishing a new non-invasive NSN concept based on electroencephalography (EEG). By combining multiple ‘smart’ mini-EEG modules into an ‘EEG sensor network’ (EEG-Net), we compensate for the lack of spatial information captured by current stand-alone mini-EEG devices, without compromising in ‘wearability’. Equipping such EEG-Nets with distributed NSP algorithms will allow to process high-density EEG data at viable energy levels, which is a game changer towards high-performance chronic EEG for, e.g., epilepsy monitoring, neuroprostheses, and BCI.

We will validate these claims in an EEG-Net prototype in the above 3 use cases, benefiting from ongoing collaborations with the KUL university hospital. In addition, to demonstrate the general applicability of our novel NSP algorithms, we will validate them in other emerging NSN types as well, such as modular or untethered neural probes.

Field of science

  • /medical and health sciences/basic medicine/neurology/epilepsy
  • /engineering and technology/electrical engineering, electronic engineering, information engineering/electronic engineering/signal processing
  • /natural sciences/computer and information sciences/data science/data analysis

Call for proposal

ERC-2018-STG
See other projects for this call

Funding Scheme

ERC-STG - Starting Grant

Host institution

KATHOLIEKE UNIVERSITEIT LEUVEN
Address
Oude Markt 13
3000 Leuven
Belgium
Activity type
Higher or Secondary Education Establishments
EU contribution
€ 1 489 656

Beneficiaries (1)

KATHOLIEKE UNIVERSITEIT LEUVEN
Belgium
EU contribution
€ 1 489 656
Address
Oude Markt 13
3000 Leuven
Activity type
Higher or Secondary Education Establishments