Deep learning in motor imagery EEG signal decoding: A Systematic Review
Thanks to the fast evolution of electroencephalography (EEG)-based brain-computer
interfaces (BCIs) and computing technologies, as well as the availability of large EEG …
interfaces (BCIs) and computing technologies, as well as the availability of large EEG …
A novel graph-based hybrid deep learning of cumulative GRU and deeper GCN for recognition of abnormal gait patterns using wearable sensors
J Wu, J Huang, X Wu, H Dai - Expert Systems with Applications, 2023 - Elsevier
In this study, a novel graph-based hybrid deep learning model of cumulative GRU and
deeper GCN was proposed to discover the most representative spatial–temporal gait …
deeper GCN was proposed to discover the most representative spatial–temporal gait …
Cross-dataset motor imagery decoding—A transfer learning assisted graph convolutional network approach
The proliferation of portable electroencephalogram (EEG) recording devices has made it
practically feasible to develop the motor imagery (MI) based brain–computer interfaces …
practically feasible to develop the motor imagery (MI) based brain–computer interfaces …
Two-Phase Multitask Autoencoder-Based Deep Learning Framework for Subject-Independent EEG Motor Imagery Classification
Electroencephalography (EEG)-based motor imagery (MI) has potential applications in
diverse fields including rehabilitation, drone control, and virtual reality. However, its practical …
diverse fields including rehabilitation, drone control, and virtual reality. However, its practical …
Optimization and Cross-Validation of Graph Neural Networks for the Diagnosis of Alzheimer's Disease
A Marmol Asenjo - 2023 - upcommons.upc.edu
This work focuses on optimizing and cross-validating Graph Neural Networks (GNNs) for the
diagnosis of Alzheimer's Disease (AD) using Electroencephalography (EEG) data. GNNs …
diagnosis of Alzheimer's Disease (AD) using Electroencephalography (EEG) data. GNNs …