Wang, Y;
Ghoreishizadeh, S;
(2025)
Classification of Individual Finger Movements from ECoG Signals using a Spiking Neural Network.
In:
Proceedings IEEE International Symposium on Circuits and Systems.
(pp. pp. 1-5).
IEEE: London, UK.
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Abstract
We present the first classifier based on a spiking neural network (SNN) that can decode individual finger movements from electrocorticography (ECoG) signals. The SNN has only six leaky integrate-and-fire neurons and uses carefully selected features: the local motor potential and the high gamma band power to analyse a publicly available ECoG dataset. Through the investigation of the key aspects affecting SNN performance (epoch length, lag time, number of concatenated epochs), the presented decoder achieves a 72.4% average classification accuracy across three subjects with an average training time of 3.55 s and a latency of only 1 ms. This work demonstrates how a simple SNN architecture can effectively decode complex motor intentions from ECoG signals, potentially enabling more efficient brain-computer interfaces.
| Type: | Proceedings paper |
|---|---|
| Title: | Classification of Individual Finger Movements from ECoG Signals using a Spiking Neural Network |
| Event: | 2025 IEEE International Symposium on Circuits and Systems (ISCAS) |
| Dates: | 25 May 2025 - 28 May 2025 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ISCAS56072.2025.11043698 |
| Publisher version: | https://doi.org/10.1109/iscas56072.2025.11043698 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | brain-computer interface (BCI), spiking neural network (SNN), motor decoding, electrocorticography (ECoG) |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10213947 |
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