Dynamic complexity in brain functional connectivity has hindered the effective use of signal processing or machine learning methods to diagnose neurological disorders such as epilepsy. This paper proposed a new
Graph Neural Network-Based EEG Classification: A Survey 2024, IEEE Transactions on Neural Systems and Rehabilitation Engineering Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities 2024, Journal of Perinatology Robust Epileptic Seizure Detection Using Long...
Klepl D, Wu M, He F (2024) Graph neural network-based EEG classification: a survey. IEEE T Neur Sys Reh 32:493–503. https://doi.org/10.1109/TNSRE.2024.3355750 Article Google Scholar Graña M, Morais-Quilez I (2023) A review of Graph Neural Networks for Electroencephalography data ...
Nowadays, semi-supervised extreme learning machine (SS-ELM) is widely used in EEG classification due to its fast training speed and good generalization performance. However, the classification performance of SS-ELM largely depends on the quality of sample graph. The graphs of most semi-supervised ...
graph pytorch representation-learning graphneuralnetwork Updated Dec 10, 2021 Python magnumical / GCN_for_EEG Star 75 Code Issues Pull requests Graph Convolutional Networks for 4-class EEG Classification machine-learning graph graph-convolutional-networks graphneuralnetwork Updated Sep 24, 2020 Py...
We introduced a multi-modal framework for inferring causal relations in brain networks, based on a graph neural network architecture, uniting structural and functional information observed with DTI and fMRI. First this model provides a data-driven perspective on a fundamental question in neuroscience, ...
Graph convolutional neural networkDifferential entropyIn recent years, graph convolutional neural networks have become research focus and inspired new ideas for emotion recognition based on EEG. Deep learning has been widely used in emotion recognition, but it is still challenging to construct models and...
Hancock. Quantum-Based Subgraph Convolutional Neural Networks. Pattern Recognition, 2019. paper Qin A, Shang Z, Tian J, et al. Spectral–Spatial Graph Convolutional Networks for Semisupervised Hyperspectral Image Classification. IEEE Geoscience and Remote Sensing Letters, 2019. paper Coley C W, Jin...
The amount of time the model considers as a single input is determined based on the frequency of the sensor and the time of real-time control. Gesture classification with Graph Neural Network A sliding-window technique is applied to augment the data. Our data overlap and are used for the ...
Graph neural networks have been successfully applied to sleep stage classification, but there are still challenges: (1) How to effectively utilize epoch information of EEG-adjacent channels owing to their different interaction effects. (2) How to extract the most representative features according to ...