Neuromorphic EMG Processing with Spiking Neural Networks

Project Information

NEPSpiNN

Grant agreement ID: 753470

DOI

10.3030/753470

Project closed

EC signature date 14 March 2017

Start date 1 September 2017

End date 31 August 2019

Funded under

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Total cost

€ 175 419,60

EU contribution

€ 175 419,60

Coordinated by

UNIVERSITAT ZURICH
Switzerland

Periodic Reporting for period 1 - NEPSpiNN (Neuromorphic EMG Processing with Spiking Neural Networks)

Reporting period: 2017-09-01 to 2019-08-31

Summary of the context and overall objectives of the project

The NEPSpiNN project aimed to realize a neuromorphic event-based neural processing system
that can directly interface with a commercial surface selectromyography (EMG) for hand gesture
classification. The proposed neuromorphic event-based processing classified the hand gestures in
a single step through a full custom hardware implementation of a spiking neural network (SNN)
which extracts spatio-temporal information of EMG signals locally in real-time with very low
power consumption.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

The general objective of the project was to exploit the low-power and low-latency features of a spiking neural network implemented in a mixed analog/digital neuromorphic chip for hand gesture classification.
Main results:
1) develop a spiking neural network able to process EMG signals
2) collection of 3 datasets (open access, zenodo) of EMG from the forearm
3) development and investigation of spiking recurrent neural network on chip
4) mobile application for sensor fusion (EMG+images) for neuroprosthetic control
5) spiking motor controller

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The project was the first using neuromorphic chip for biomedical applications. It was the first one that was using hardware spiking neural network for EMG classification.
The results of the project have high research and commercial exploitation potentialities. The research area of exploitation was primary neuromorphic engineering and neurorobotics. The project results are a step towards the applications of neuromorphic devices in real-world scenarios, in particular for biomedical applications. This will have a high impact on health and science. The commercial areas of exploitation will be information technology, biomedical applications, prosthetic devices, and real-time computing. During the project development, the researcher already established collaborations with industrial partners that can result in new patents and technology transfer to products that can be introduced in the market.

General project description