A Spiking Neural Network With Adaptive Graph Convolution and LSTM for EEG-Based Brain-Computer Interfaces

A Spiking Neural Network With Adaptive Graph Convolution and LSTM for EEG-Based Brain-Computer Interfaces

Electroencephalography (EEG) signals classification is essential for the brain-computer interface (BCI). Recently, energy-efficient spiking neural networks (SNNs) have shown great potential in EEG analysis due to their ability to capture the complex dynamic properties of biological neurons while als...

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Bibliographic Details
Main Authors: Peiliang Gong, Pengpai Wang, Yueying Zhou, Daoqiang Zhang
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Transactions on Neural Systems and Rehabilitation Engineering
Subjects:
EEG classification
brain-computer interface
spiking neural network
graph convolution
LSTM
Online Access:https://ieeexplore.ieee.org/document/10049464/
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