https://github.com/MAMEM/eeg-processing-toolbox
Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs
Papers with Code 아카이브
Electroencephalogram (EEG)
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- 평가표
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- 보존된 평가 기록
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Electroencephalogram (EEG) is a method of recording brain activity using electrophysiological indexes. When the brain is active, a large number of postsynaptic potentials generated synchronously by neurons are formed after summation. It records the changes of electric waves during brain activity and is the overall reflection of the electrophysiological activities of brain nerve cells on the surface of cerebral cortex or scalp. Brain waves originate from the postsynaptic potential of the apical dendrites of pyramidal cells. The formation of synchronous rhythm of EEG is also related to the activity of nonspecific projection system of cortex and thalamus. EEG is the basic theoretical research of brain science. EEG monitoring is widely used in its clinical application.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MAMEM/ssvep-eeg-processing-toolbox
Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs
https://github.com/MeetXinZhang/EEG-ConvTransformer
EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli Classification
https://github.com/MichaelMurashov/ecg-testing
EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals
https://github.com/MindCode-4/code-13/tree/main/SGDR
https://github.com/MindCode-4/code-6/tree/main/Black-Box-Attacks-against
https://github.com/MindCode-4/code-9/tree/main/SGDR
https://github.com/MindSpore-scientific-2/code-11/tree/main/RGNN
GNN4EEG: A Benchmark and Toolkit for Electroencephalography Classification with Graph Neural Network
https://github.com/MindSpore-scientific-2/code-8/tree/main/RGNN
LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface
https://github.com/MindSpore-scientific/code-1/tree/main/RGNN
GNN4EEG: A Benchmark and Toolkit for Electroencephalography Classification with Graph Neural Network
https://github.com/MindSpore-scientific/code-13/tree/main/RGNN
https://github.com/MindSpore-scientific/code-14/tree/main/TSFF
https://github.com/MindSpore-scientific/code-2/tree/main/TSFF
Time-space-frequency feature Fusion for 3-channel motor imagery classification
https://github.com/MindSpore-scientific/code-8/tree/main/RGNN
EEG-Based Emotion Recognition Using Regularized Graph Neural Networks
https://github.com/Mingyuan-Zhu/BCI_practice
Deep learning-based electroencephalography analysis: a systematic review
https://github.com/MohammadJavadD/APD_EEG
Amplifying Pathological Detection in EEG Signaling Pathways through Cross-Dataset Transfer Learning
https://github.com/MousaviSajad/SleepEEGNet
SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
https://github.com/MrtnMndt/Rethinking_CNN_Layerwise_Feature_Amounts
https://github.com/Nikeshbajaj/Notebooks/tree/master/PhyAAt_Notebooks
https://github.com/Nikeshbajaj/phyaat
https://github.com/PatriciaRodrigues1994/pytorch-facebook-udacity-challenge
https://github.com/RuiLiu-cc/FLEEG
Aggregating Intrinsic Information to Enhance BCI Performance through Federated Learning
https://github.com/SandraPrestel/deep-sleep-transfer
https://github.com/SevenZhan/Pytorch
https://github.com/Sharad24/Epileptic-Seizure-Detection
Learning Robust Features using Deep Learning for Automatic Seizure Detection
https://github.com/Timothy102/EEG-Sleep
Sleep syndromes onset detection based on automatic sleep staging algorithm
https://github.com/UKBWorks/AccSleepNet
DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
https://github.com/VDelv/EEGLearn-Pytorch
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
https://github.com/ViacheslavBobrov/EEG_RNN_Conv_Learning
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
https://github.com/WoongheeLee/DeepSleepNet_Implementation_in_Keras
DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
https://github.com/WoongheeLee/SleepEEGNet_in_TensorFlow2
SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
https://github.com/YangWangsky/tf_EEGLearn
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
https://github.com/YundongWang/BCI_Challenge
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
https://github.com/abhuse/cyclic-cosine-decay
https://github.com/adwaykanhere/FYP
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
https://github.com/aguscerdo/EE239AS-Project
ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.