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Neuromorphic computing system for real-time signal monitoring and classification with ultra-low-power 2D devices

Project description

Low-energy, high-accuracy wearable devices in electrophysiological signal classification

Near-real-time analysis of electroencephalography and electromyography recordings are valuable in health monitoring and personalised medicine as well as in the development of brain-computer interfaces. Most technologies rely on inefficient microelectronics and cloud computing lacking the desired signal classification accuracy. Neuromorphic computing mimicking the brain’s neural signal processing could provide a solution to the computational and energy inefficiency of current technologies and their resulting limits in classification accuracy. The ERC-funded NEURO2D project aims to develop an innovative class of 2D ‘charge trap memory’ (2D-CTM) neuromorphic devices leveraging reservoir computing. The low-power, high-classification-accuracy 2D-CTM technology could lead to scalable, low-power implantable and wearable chips for real-time electrophysiological signal monitoring and classification.

Objective

The detection and classification of electrophysiological signals (EPSs), such as
electroencephalography (EEG) and electromyography (EMG) recordings, are the gold standard in
neuroscience, enabling the identification of digital biomarkers capable of health monitoring,
personalised medicine and advanced brain-computer interfaces (BCIs). The state-of-the-art
technology in this field, however, still relies on bulky, inefficient microelectronic systems which
relies on artificial intelligence (AI) in the cloud. The energy efficiency and classification accuracy
can be largely improved by neuromorphic computing with emerging materials and devices capable
of mimicking the neural mechanisms in our brain. This project aims at developing a novel class of
neuromorphic systems based on reservoir computing (RC) in charge trap memory (CTM) based on
2D semiconductors. 2D-CTM devices are able to extracted features from EPSs at extremely low
power and high accuracy of classification, thus providing efficient biomarkers for medical diagnosis
and BCIs. The project will develop the RC system based on the 2D-CTM technology for a broad
application space, with the goal of establishing a novel technology platform for scalable, lowpower implantable/wearable chips for real-time EPS monitoring and classification.

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Topic(s)

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HORIZON-ERC-POC - HORIZON ERC Proof of Concept Grants

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Call for proposal

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(opens in new window) ERC-2024-POC

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Host institution

POLITECNICO DI MILANO
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 150 000,00
Address
PIAZZA LEONARDO DA VINCI 32
20133 Milano
Italy

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Region
Nord-Ovest Lombardia Milano
Activity type
Higher or Secondary Education Establishments
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Total cost

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Beneficiaries (1)

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