Summary
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.
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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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101189508 |
| Start date: | 01-10-2024 |
| End date: | 31-03-2026 |
| Total budget - Public funding: | - 150 000,00 Euro |
Cordis data
Original description
The detection and classification of electrophysiological signals (EPSs), such aselectroencephalography (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.
Status
SIGNEDCall topic
ERC-2024-POCUpdate Date
24-11-2024
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