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BrainControl

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Cerrado

Stable Brain Machine control via a learnable standalone interface

Non-invasive Brain Machine Interfaces (BMI) bring great promise for neuro-rehabilitation and neuro-prosthesis, as well as for brain control of everyday devices and performance of simple tasks. Over the last 15 years the interest i... ver más

28/02/2018

FUNDACAO CHAMPALIM...

150K€

Presupuesto del proyecto: 150K€

Líder del proyecto

FUNDACAO D. ANNA DE SOMMER CHAMPALIMAUD E DR.... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

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Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2018-02-28

ERC-PoC-2015: ERC Proof of Concept Grant

I+D

Cerrada hace 9 años

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No hay información privada compartida para este proyecto. Habla con el coordinador.

2 Participantes

FUNDACAO CHAMPALIMAUD

149.63K€ | Lider

TIMAC AGRO ESPAÑA

246.30K€ | Participante

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Duración del proyecto: 18 meses Fecha Inicio: 2016-08-01
Fecha Fin: 2018-02-28

Líder del proyecto

FUNDACAO D. ANNA DE SOMMER CHAMPALIMAUD E DR.... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 150K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Non-invasive Brain Machine Interfaces (BMI) bring great promise for neuro-rehabilitation and neuro-prosthesis, as well as for brain control of everyday devices and performance of simple tasks. Over the last 15 years the interest in BMIs has grown substantially, and a variety of interfaces have been developed. The field has been growing dramatically, and market studies reveal an estimated market size of $1.46 billion by 2020. However, non-invasive BMIs have failed to reach the impressive control seen by BMIs implanted in the brain. To date, they require considerable training to reach a moderate level of control, they are susceptible to noise and interference, do not generalize between people and devices, and performance does not show long-term consolidation. Results from our ERC-funded work uncovered a new paradigm that dramatically improves these issues. We propose to develop a prototype for a novel, standalone, non-invasive, noise-resistant BMI, based on an unexplored BMI learning paradigm. In this POC we will 1) refine the brain signal interface (decoder) to be automatically customizable to each individual and produces faster training, 2) implement our BMI technology into a portable hardware-based system, and 3) develop a virtual reality/gaming training platform that will increase learning, performance and consolidation of BMI control. In addition to these technical aims, we propose to explore commercial opportunities and societal benefits, in particular in the health sector. We will conduct market analysis and develop a business case for this product, while expanding industry contacts for production and commercialization. The work proposed in this PoC grant will permit, for the first time to our knowledge, the development of a portable, stand-alone, noise-resistant, and easy to learn BMI, applicable across a wide set of devices, which will bring a significant social impact in health, entertainment and other applications.

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