Papers with Code 아카이브
Electroencephalogram (EEG)
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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/IA3005/Elliptical-Wishart-for-Signal-Processing
공식 코드https://github.com/IS-UMK/supFunSim
공식 코드Localization of Brain Activity from EEG/MEG Using MV-PURE Framework
https://github.com/KDongYoung/Calibration-Free-Driver-Drowsiness-Classification-based-on-Manifold-Level-Augmentation
공식 코드Calibration-Free Driver Drowsiness Classification based on Manifold-Level Augmentation
https://github.com/Kaist-ICLab/K-EmoCon_SupplementaryCodes
공식 코드https://github.com/LorenaGallego/DL4ADpred
공식 코드https://github.com/MDFahimAnjum/LightCNNforPD
공식 코드Parkinson's Disease Classification via EEG: All You Need is a Single Convolutional Layer
https://github.com/MHersche/HDembedding-BCI
공식 코드https://github.com/MSA-LMC/PhysioSync
공식 코드https://github.com/MathieuSeraphim/SPDTransNet_plus
공식 코드https://github.com/Metaverse-Crowdsource/EEG-Chaos-Kuramoto-Neural-Net
공식 코드Artificial Intelligence for EEG Prediction: Applied Chaos Theory
https://github.com/MiriamKirchhoff/BO_for_EEG-TMS_phase
공식 코드Closed-Loop phase selection in EEG-TMS using Bayesian Optimization
https://github.com/Mithunjha/EarEEG_KnowledgeDistillation
공식 코드A Knowledge Distillation Framework For Enhancing Ear-EEG Based Sleep Staging With Scalp-EEG Data
https://github.com/MultiScale-BCI/IV-2a
공식 코드https://github.com/N-Nieto/Relevance_Based_Pruning
공식 코드Extreme Learning Machine design for dealing with unrepresentative features
https://github.com/NeuroTechX/moabb
공식 코드The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark
https://github.com/Niloy333/eegFloss
공식 코드eegFloss: A Python package for refining sleep EEG recordings using machine learning models
https://github.com/Ori226/p300_lstm
공식 코드https://github.com/PedroFerreiradaCosta/NeuroadaptiveEEG
공식 코드Neuroadaptive electroencephalography: a proof-of-principle study in infants
https://github.com/PieraRiccio/Emotions-fromEEG-toArt
공식 코드https://github.com/Prog-Jacob/watermarking-eeg-models
공식 코드Protecting Intellectual Property of EEG-based Neural Networks with Watermarking
https://github.com/RomGai/BrainVis
공식 코드BrainVis: Exploring the Bridge between Brain and Visual Signals via Image Reconstruction
https://github.com/SPEECHCOG/PFML
공식 코드PFML: Self-Supervised Learning of Time-Series Data Without Representation Collapse
https://github.com/SPOClab-ca/BENDR
공식 코드https://github.com/SajadMo/SleepEEGNet
공식 코드SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
https://github.com/SeyedMuhammadHosseinMousavi/Synthetic-Data-Generation-by-Supervised-Neural-Gas-Network
공식 코드https://github.com/ShiyaLiu/EEG-feature-filter-and-disguising
공식 코드https://github.com/SiavashShams/neuro2semantic
공식 코드https://github.com/Singular-Brain/CCSPNet
공식 코드Towards Real-World BCI: CCSPNet, A Compact Subject-Independent Motor Imagery Framework
https://github.com/SuperBruceJia/EEG-DL
공식 코드https://github.com/Surayez/EmotivDriverDistraction
공식 코드Classification of multivariate weakly-labelled time-series with attention
https://github.com/TSTEP99/tensor_decomp_eeg
공식 코드Tensor Decomposition of Large-scale Clinical EEGs Reveals Interpretable Patterns of Brain Physiology
https://github.com/TencentARC/ViT-Lens
공식 코드https://github.com/TianwangJia/FedBS
공식 코드Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces
https://github.com/Tolmez/DivFE
공식 코드Classification of Motor Imagery EEG Signals by Using a Divergence Based Convolutional Neural Network
https://github.com/Tolmez/WalshDNN
공식 코드Strengthening the Training of Convolutional Neural Networks By Using Walsh Matrix
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