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Graph convolution network-based eeg signal analysis: a review
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motor imagery brain-computer interfaces (MI-BCIs). However, decoding intentions from MI …
motor imagery brain-computer interfaces (MI-BCIs). However, decoding intentions from MI …
Wasserstein generative adversarial network with gradient penalty and convolutional neural network based motor imagery EEG classification
H **ong, J Li, J Liu, J Song, Y Han - Journal of Neural …, 2024 - iopscience.iop.org
Objective. Due to the difficulty in acquiring motor imagery electroencephalography (MI-EEG)
data and ensuring its quality, insufficient training data often leads to overfitting and …
data and ensuring its quality, insufficient training data often leads to overfitting and …
Enhancing motor imagery classification: a novel CNN with self-attention using local and global features of filtered EEG data
Motor imagery (MI)-based brain computer interfaces (BCIs) frequently use convolutional
neural networks (CNNs) to analyse electroencephalography (EEG) signals. In this study, we …
neural networks (CNNs) to analyse electroencephalography (EEG) signals. In this study, we …
[HTML][HTML] Adaptive GCN and Bi-GRU-Based Dual Branch for Motor Imagery EEG Decoding
Y Wu, P Cao, M Xu, Y Zhang, X Lian, C Yu - Sensors, 2025 - mdpi.com
Decoding motor imagery electroencephalography (MI-EEG) signals presents significant
challenges due to the difficulty in capturing the complex functional connectivity between …
challenges due to the difficulty in capturing the complex functional connectivity between …