EEGSN: Towards Efficient Low-latency Decoding of EEG with Graph Spiking
Neural Networks
- URL: http://arxiv.org/abs/2304.07655v2
- Date: Tue, 18 Apr 2023 20:49:06 GMT
- Title: EEGSN: Towards Efficient Low-latency Decoding of EEG with Graph Spiking
Neural Networks
- Authors: Xi Chen, Siwei Mai, Konstantinos Michmizos
- Abstract summary: A majority of neural networks (SNNs) are trained based on inductive biases that are not necessarily a good fit for several critical tasks that require low-latency and power efficiency.
Here, we propose a graph spiking neural architecture for multi-channel EEG classification (EEGS) that learns the dynamic relational information present in the distributed EEG sensors.
Our method reduced the inference computational complexity by $times 20$ compared to the state-the-art SNNs, while achieved comparable accuracy on motor execution tasks.
- Score: 4.336065967298193
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A vast majority of spiking neural networks (SNNs) are trained based on
inductive biases that are not necessarily a good fit for several critical tasks
that require low-latency and power efficiency. Inferring brain behavior based
on the associated electroenchephalography (EEG) signals is an example of how
networks training and inference efficiency can be heavily impacted by learning
spatio-temporal dependencies. Up to now, SNNs rely solely on general inductive
biases to model the dynamic relations between different data streams. Here, we
propose a graph spiking neural network architecture for multi-channel EEG
classification (EEGSN) that learns the dynamic relational information present
in the distributed EEG sensors. Our method reduced the inference computational
complexity by $\times 20$ compared to the state-of-the-art SNNs, while achieved
comparable accuracy on motor execution classification tasks. Overall, our work
provides a framework for interpretable and efficient training of graph spiking
networks that are suitable for low-latency and low-power real-time
applications.
Related papers
- Unveiling the Potential of Spiking Dynamics in Graph Representation Learning through Spatial-Temporal Normalization and Coding Strategies [15.037300421748107]
spiking neural networks (SNNs) have attracted substantial interest due to their potential to replicate the energy-efficient and event-driven processing of neurons.
This work examines the unique properties and benefits of spiking dynamics in enhancing graph representation learning.
We propose a spike-based graph neural network model that incorporates spiking dynamics, enhanced by a novel spatial-temporal feature normalization (STFN) technique.
arXiv Detail & Related papers (2024-07-30T02:53:26Z) - Label Deconvolution for Node Representation Learning on Large-scale
Attributed Graphs against Learning Bias [75.44877675117749]
We propose an efficient label regularization technique, namely Label Deconvolution (LD), to alleviate the learning bias by a novel and highly scalable approximation to the inverse mapping of GNNs.
Experiments demonstrate LD significantly outperforms state-of-the-art methods on Open Graph datasets Benchmark.
arXiv Detail & Related papers (2023-09-26T13:09:43Z) - How neural networks learn to classify chaotic time series [77.34726150561087]
We study the inner workings of neural networks trained to classify regular-versus-chaotic time series.
We find that the relation between input periodicity and activation periodicity is key for the performance of LKCNN models.
arXiv Detail & Related papers (2023-06-04T08:53:27Z) - Intelligence Processing Units Accelerate Neuromorphic Learning [52.952192990802345]
Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency.
We present an IPU-optimized release of our custom SNN Python package, snnTorch.
arXiv Detail & Related papers (2022-11-19T15:44:08Z) - Spiking Variational Graph Auto-Encoders for Efficient Graph
Representation Learning [10.65760757021534]
We propose an SNN-based deep generative method, namely the Spiking Variational Graph Auto-Encoders (S-VGAE) for efficient graph representation learning.
We conduct link prediction experiments on multiple benchmark graph datasets, and the results demonstrate that our model consumes significantly lower energy with the performances superior or comparable to other ANN- and SNN-based methods for graph representation learning.
arXiv Detail & Related papers (2022-10-24T12:54:41Z) - EEG-BBNet: a Hybrid Framework for Brain Biometric using Graph
Connectivity [1.1498015270151059]
We present EEG-BBNet, a hybrid network which integrates convolutional neural networks (CNN) with graph convolutional neural networks (GCNN)
Our models outperform all baselines in the event-related potential (ERP) task with an average correct recognition rates up to 99.26% using intra-session data.
arXiv Detail & Related papers (2022-08-17T10:18:22Z) - Characterizing Learning Dynamics of Deep Neural Networks via Complex
Networks [1.0869257688521987]
Complex Network Theory (CNT) represents Deep Neural Networks (DNNs) as directed weighted graphs to study them as dynamical systems.
We introduce metrics for nodes/neurons and layers, namely Nodes Strength and Layers Fluctuation.
Our framework distills trends in the learning dynamics and separates low from high accurate networks.
arXiv Detail & Related papers (2021-10-06T10:03:32Z) - Emotional EEG Classification using Connectivity Features and
Convolutional Neural Networks [81.74442855155843]
We introduce a new classification system that utilizes brain connectivity with a CNN and validate its effectiveness via the emotional video classification.
The level of concentration of the brain connectivity related to the emotional property of the target video is correlated with classification performance.
arXiv Detail & Related papers (2021-01-18T13:28:08Z) - Binary Graph Neural Networks [69.51765073772226]
Graph Neural Networks (GNNs) have emerged as a powerful and flexible framework for representation learning on irregular data.
In this paper, we present and evaluate different strategies for the binarization of graph neural networks.
We show that through careful design of the models, and control of the training process, binary graph neural networks can be trained at only a moderate cost in accuracy on challenging benchmarks.
arXiv Detail & Related papers (2020-12-31T18:48:58Z) - Progressive Tandem Learning for Pattern Recognition with Deep Spiking
Neural Networks [80.15411508088522]
Spiking neural networks (SNNs) have shown advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency.
We propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition.
arXiv Detail & Related papers (2020-07-02T15:38:44Z) - GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding
Time-resolved EEG Motor Imagery Signals [8.19994663278877]
A novel deep learning framework based on the graph convolutional neural networks (GCNs) is presented to enhance the decoding performance of raw EEG signals.
The introduced approach has been shown to converge for both personalized and group-wise predictions.
arXiv Detail & Related papers (2020-06-16T04:57:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.