May 6, 2023 · Specifically, the graph attention network (GAT) extracts spatial features at the front end, and Transformer gets time features as the back end.
In this paper, we put forward HAN for EEG-based seizure detection. The multi-channel EEG signal is modeled as a graph and GAT is employed to extract the spatial ...
HAN leverages the attention mechanism and fully extracts the spatial-temporal correlation of EEG signals. The focal loss function is introduced to HAN to deal ...
HAN leverages the attention mechanism and fully extracts the spatial-temporal correlation of EEG signals. The focal loss function is introduced to HAN to deal ...
In this study, we introduce a novel hybrid deep learning architecture, merging DenseNet and Vision Transformer (ViT) with an attention fusion layer for seizure ...
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We propose a Residual-based Inception with Hybrid-Attention Network(RIHANet) to achieve automatic seizure detection.
Nov 26, 2023 · This study proposes a novel epileptic seizure classification method using EEG based on a hybrid time-frequency attention deep network.
A scheme combining feature fusion and hybrid deep learning models ...
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Jul 23, 2024 · A multi-channel feature fusion CNN-Bi-LSTM epilepsy EEG classification and prediction model based on attention mechanism. IEEE Access 11 ...
Aug 21, 2024 · This work proposes a robust deep learning framework called HyEpiSeiD that extracts self-trained features from the pre-processed EEG signals.
We propose a Residual-based Inception with Hybrid-Attention Network(RIHANet) to achieve automatic seizure detection.
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