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DISPATCH Neuro-Sense

Financiado

Distributed Signal Processing Algorithms for Chronic Neuro Sensor Networks

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 int... ver más

30/06/2024

KU Leuven

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

KATHOLIEKE UNIVERSITEIT LEUVEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 2 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2024-06-30

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

ERC-2018-STG: ERC Starting Grant

I+D

Cerrada hace 7 años

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2 Participantes

KU Leuven

1.49M€ | Lider

SEGURIDAD INDUSTRIAL SA

325.14K€ | Participante

Últimas noticias

01-12-2024: REDES En las últimas 48 horas el Organismo REDES ha otorgado 1337 concesiones

01-12-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

01-12-2024: AEI En las últimas 48 horas el Organismo AEI ha otorgado 23 concesiones

Duración del proyecto: 66 meses Fecha Inicio: 2018-12-19
Fecha Fin: 2024-06-30

Líder del proyecto

KATHOLIEKE UNIVERSITEIT LEUVEN

TRL 4-5

Presupuesto del proyecto 1M€

Descripción del proyecto 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.

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