EEG Deep Learning Library
EEG-DL is a Deep Learning (DL) library written by TensorFlow for EEG Tasks (Signals) Classification. The platform provides access to the most advanced deep learning algorithms, which are regularly updated to ensure their effectiveness.
Related Work:
1. A Novel Approach of Decoding EEG Four-class Motor Imagery Tasks via Scout ESI and CNN
Link: https://iopscience.iop.org/article/10.10...
2. GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals
Link: https://ieeexplore.ieee.org/document/988...
3. Deep Feature Mining via Attention-based BiLSTM-GCN for Human Motor Imagery Recognition
Link: https://www.frontiersin.org/articles/10....
4. Attention-based Graph ResNet for Motor Intent Detection from Raw EEG signals
Link: https://arxiv.org/abs/2007.13484
Related Work:
1. A Novel Approach of Decoding EEG Four-class Motor Imagery Tasks via Scout ESI and CNN
Link: https://iopscience.iop.org/article/10.10...
2. GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals
Link: https://ieeexplore.ieee.org/document/988...
3. Deep Feature Mining via Attention-based BiLSTM-GCN for Human Motor Imagery Recognition
Link: https://www.frontiersin.org/articles/10....
4. Attention-based Graph ResNet for Motor Intent Detection from Raw EEG signals
Link: https://arxiv.org/abs/2007.13484
Specifications
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