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EEGInfantCogDgTool

Financiado

A tool to detect cognitive abnormalities in the first year of life based on elec...

A tool to detect cognitive abnormalities in the first year of life based on electroencephalography (EEG) Due to motor immaturity and a lack of language, it is difficult to assess cognitive functions in the first year of life, making it difficult to properly and carefully follow up on at-risk children such as premature infants (8-10%... ver más

31/03/2025

CEA

150K€

Presupuesto del proyecto: 150K€

Líder del proyecto

COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENER... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-07-24

ERC-2023-POC: ERC PROOF OF CONCEPT GRANTS Objective:Objectives:The ERC Proof of Concept Grants aim at facilitating exploration of the commerci...

I+D

Cerrada hace 1 año

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1 Participantes

CEA

150.00K€ | Lider

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Duración del proyecto: 20 meses Fecha Inicio: 2023-07-24
Fecha Fin: 2025-03-31

Líder del proyecto

COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENER... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 150K€

Descripción del proyecto Due to motor immaturity and a lack of language, it is difficult to assess cognitive functions in the first year of life, making it difficult to properly and carefully follow up on at-risk children such as premature infants (8-10% of births), thus delaying diagnosis and adequate support. Through the babylearn project, I developed a panel of innovant EEG procedures and experimental paradigms to explore the infant developing brain circuits and learning skills. I therefore propose to extend this expertise to a clinical setting and provide a tool based on EEG for a comprehensive assessment of infant cognitive development. After building an effective prototype to test key cognitive functions (syllable and face perception, temporal anticipation) and a critical learning mechanism (statistical learning) in infants while recording EEG, we will assess in the lab the stability and robustness of our measures (TRL level 4), then extend the test in a clinical setting with a normal (full-term neonates) and at-risk population (premature neonates) (TRL level 5-6). We will validate the diagnostic utility of the device through follow-up of infants in neonatology departments. Our proposed tool aims to significantly improve the diagnosis of neurodevelopmental disorders (NDD) in infants by allowing earlier identification in the clinically silent period of the first year. With accurate and objective measures of infant cognitive development, this tool will enable health care professionals to provide targeted and effective care, giving children the best chance of success.

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