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Neuromorphic EMG Processing with Spiking Neural Networks

Total Cost €


EC-Contrib. €






Project "NEPSpiNN" data sheet

The following table provides information about the project.


Organization address
address: RAMISTRASSE 71
city: Zürich
postcode: 8006

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Switzerland [CH]
 Total cost 175˙419 €
 EC max contribution 175˙419 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2016
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2017
 Duration (year-month-day) from 2017-09-01   to  2019-08-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITAT ZURICH CH (Zürich) coordinator 175˙419.00


 Project objective

The aim of the NEPSpiNN project is to realize a neuromorphic event-based neural processing system that can directly interface with a commercial surface electromyography (sEMG) for the extraction of signal features and classification of the motor neurons output activities. The sEMG is a non-invasive method for measuring the electrical activity, associated to the muscle activities, by means of surface electrodes located above the skin.The amplitude of the sEMG signals, measured in this way, correlates with the number of action potentials discharged by a population of activated motor neurons. To understand the muscular behavior, several measurements are required, which produce a large amount of data, typically, processed by external computers. This makes a wearable solution difficult. In addition, in the current state-of-the-art the sEMG data analyses is composed by three different steps: features extraction, moto neurons outputs discrimination and classification. The steps are activated in sequence increasing the time required for the analyses, that is a problem in real-time applications. The NEPSpiNN project proposes a sEMG analyses stage, implemented in a compact ultra-low power neuromorphic chip, to be able to process data in real-time with low-latency, useful for future implementation of wearable devices. A full custom hardware implementation of a deep neural network (DNN), implemented on neuromorphic spiking neural processing circuits, will classify the motor activities in real time, to find the input for a control system of an external device (e.g. prostesis or exoskeleton). The integration on a unique portable device will allow to decrease the computational cost of processing and the power consumption. This enables a system that can be integrated in a wearable solution without the necessity to transmit data to a remote host.


year authors and title journal last update
List of publications.
2019 Enea Ceolini, Gemma Taverni, Lyes Khacef, Melika Payvand, Elisa Donati
Sensor fusion using EMG and vision for hand gesture classification in mobile applications
published pages: , ISSN: , DOI:
2018 Elisa Donati, Melika Payvand, Nicoletta Risi, Renate Krause, Karla Burelo, Giacomo Indiveri, Thomas Dalgaty, Elisa Vianello
Processing EMG signals using reservoir computing on an event-based neuromorphic system
published pages: , ISSN: , DOI:
2018 Elisa Donati, Fernando Perez-Peña, Chiara Bartolozzi, Giacomo Indiveri, Elisabetta Chicca
Open-loop neuromorphic controller implemented on vlsi devices
published pages: , ISSN: , DOI:
2019 Elisa Donati, Melika Payvand, Nicoletta Risi, Renate Barbara Krause, Giacomo Indiveri
Discrimination of EMG Signals Using a Neuromorphic Implementation of a Spiking Neural Network
published pages: , ISSN: 1932-4545, DOI:
IEEE transactions on biomedical circuits and systems 2019-11-07

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The information about "NEPSPINN" are provided by the European Opendata Portal: CORDIS opendata.

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