https://github.com/CECNL/ExBrainable
공식 코드ExBrainable: An Open-Source GUI for CNN-based EEG Decoding and Model Interpretation
Papers with Code 아카이브
Eeg Decoding
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EEG Decoding - extracting useful information directly from EEG data.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/DashanGao/Federated-Transfer-Learning-for-EEG
공식 코드https://github.com/anranknight/EEG-Transformer
공식 코드Transformer-based Spatial-Temporal Feature Learning for EEG Decoding
https://github.com/braindecode/braindecode
공식 코드Deep learning with convolutional neural networks for EEG decoding and visualization
https://github.com/carraraig/Phase-SPDNet
공식 코드Geometric Neural Network based on Phase Space for BCI-EEG decoding
https://github.com/cecnl/eeg-kd
공식 코드https://github.com/cecnl/matt
공식 코드https://github.com/cuijiancorbin/A-Compact-and-Interpretable-Convolutional-Neural-Network-for-Single-Channel-EEG
공식 코드https://github.com/dcwil/eegspdnet
공식 코드https://github.com/joshparksj/eeg-wgan
공식 코드Improving EEG Signal Classification Accuracy Using Wasserstein Generative Adversarial Networks
https://github.com/kolcs/bionic_apps
공식 코드https://github.com/kovalalvi/beira
공식 코드https://github.com/miaozhengqing/lmda-code
공식 코드Time-space-frequency feature Fusion for 3-channel motor imagery classification
https://github.com/miaozhengqing/tsff
공식 코드Time-space-frequency feature Fusion for 3-channel motor imagery classification
https://github.com/miaozhengqing/weightfreezing
공식 코드https://github.com/navidfoumani/eeg2rep
공식 코드EEG2Rep: Enhancing Self-supervised EEG Representation Through Informative Masked Inputs
https://github.com/tidiane-camaret/eegclip
공식 코드EEG-CLIP : Learning EEG representations from natural language descriptions
https://github.com/tiehangd/ue-eeg
공식 코드Uncertainty Detection and Reduction in Neural Decoding of EEG Signals
https://github.com/wjq-learning/cbramod
공식 코드CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding
https://github.com/wzwvv/DBConformer
공식 코드DBConformer: Dual-Branch Convolutional Transformer for EEG Decoding
https://github.com/wzwvv/MCNet
공식 코드MVCNet: Multi-View Contrastive Network for Motor Imagery Classification
https://github.com/ADMoreau/EEG-Research
Deep learning with convolutional neural networks for EEG decoding and visualization
https://github.com/MindSpore-scientific/code-2/tree/main/TSFF
Time-space-frequency feature Fusion for 3-channel motor imagery classification
https://github.com/RuiLiu-cc/FLEEG
Aggregating Intrinsic Information to Enhance BCI Performance through Federated Learning
https://github.com/eeyhsong/eeg-transformer
Transformer-based Spatial-Temporal Feature Learning for EEG Decoding
https://github.com/gibranfp/P300-CNNT
Deep learning with convolutional neural networks for EEG decoding and visualization
https://github.com/kamalravi/Imagined-Speech-Classification-using-EEG-
https://github.com/lclarte/dreemmva
Deep learning with convolutional neural networks for EEG decoding and visualization
https://github.com/okbalefthanded/ssvepformer
A Transformer-based deep neural network model for SSVEP classification
https://github.com/rczhen/Movement-Classification-based-on-Electroencephalography-EEG-Signals
Deep learning with convolutional neural networks for EEG decoding and visualization
https://github.com/teptwomey/deep_learning_architectures_for_fscv
Transformer-based Spatial-Temporal Feature Learning for EEG Decoding
https://github.com/zacharycbrown/ssl_baselines_for_biosignal_feature_extraction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.