Different from the traditional graph convolutional neural networks (GCNN) methods, the proposed DGCNN method can dynamically learn the intrinsic relationship between different electroencephalogram ...

This method incorporates three graph learning approaches to systematically assess the connectivity and synchronization of multi-channel EEG signals. The multi-branch graph convolutional network is ...

In this study, we address the challenges by proposing a feature fusion model that integrates graph convolutional network and bidirectional long short-term memory network for enhanced knot ...

To address the above problem, we propose a new fatigue driving detection network, referred to as the attention-based multi-semantic dynamical graph convolutional network (AMD-GCN). First, the network ...

Based on the absolute Pearson's matrix of overall signals, the graph Laplacian of EEG electrodes was built up. The GCNs-Net constructed by graph convolutional layers learns the generalized features.

Toward the development of effective and efficient brain–computer interface (BCI) systems, precise decoding of brain activity measured by an electroencephalogram (EEG) is highly demanded. Traditional ...

eeg classification attention convolutional-neural-networks motor-imagery temporal-convolutional-network multi-head-self-attention Updated on Mar 22 Python ...