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EEG

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/chamwen/MEKT

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Manifold Embedded Knowledge Transfer for Brain-Computer Interfaces

https://github.com/chen-xdu/gmss

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GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition

https://github.com/chengstark/interp.-eeg-public

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Improving Clinician Performance in Classification of EEG Patterns on the Ictal-Interictal-Injury Continuum using Interpretable Machine Learning

https://github.com/chenxiachan/eegnex

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Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX

https://github.com/clbonet/Sliced-Wasserstein_on_Symmetric_Positive_Definite_Matrices

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Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals

https://github.com/clbonet/spdsw

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Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals

https://github.com/cnnp-lab/2024_asm_eeg

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Anti-seizure medication tapering correlates with daytime delta band power reduction in the cortex

https://github.com/cnnp-lab/2025_asm-szstate_gmb

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Dual mechanism of Anti-Seizure Medications in controlling seizure activity

https://github.com/cnnp-lab/seizure_severity_library

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A library of quantitative markers of seizure severity

https://github.com/comp-imaging-sci/attention-based-bilstm-sleep-scoring

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Attention-Based CNN-BiLSTM for Sleep State Classification of Spatiotemporal Wide-Field Calcium Imaging Data

https://github.com/coreylammie/memristive-seizure-detection-and-prediction-by-parallel-convolutional-neural-networks

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Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks

https://github.com/cuijiancorbin/A-Compact-and-Interpretable-Convolutional-Neural-Network-for-Single-Channel-EEG

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A Compact and Interpretable Convolutional Neural Network for Cross-Subject Driver Drowsiness Detection from Single-Channel EEG

https://github.com/cuijiancorbin/Subject-Independent-Drowsiness-Recognition-from-Single-Channel-EEG-with-an-Interpretable-CNN-LSTM

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Subject-Independent Drowsiness Recognition from Single-Channel EEG with an Interpretable CNN-LSTM model

https://github.com/cuijiancorbin/Towards-Best-Practice-of-Interpreting-Deep-Learning-Models-for-EEG-based-BCI

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Towards Best Practice of Interpreting Deep Learning Models for EEG-based Brain Computer Interfaces

https://github.com/cuijiancorbin/eeg-based-cross-subject-driver-drowsiness-recognition-with-an-interpretable-cnn

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EEG-based Cross-Subject Driver Drowsiness Recognition with an Interpretable Convolutional Neural Network

https://github.com/danielathome19/Pianoid-EEG-NN

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Simulation of Neural Responses to Classical Music Using Organoid Intelligence Methods

https://github.com/danielathome19/pyorganoid

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Simulation of Neural Responses to Classical Music Using Organoid Intelligence Methods

https://github.com/dapascual/GAN_epilepsy

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Synthetic Epileptic Brain Activities Using Generative Adversarial Networks

https://github.com/davidruegamer/BoostingSignalSynchro

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Boosting Factor-Specific Functional Historical Models for the Detection of Synchronisation in Bioelectrical Signals

https://github.com/dcwil/eegspdnet

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Deep Riemannian Networks for End-to-End EEG Decoding

https://github.com/deepBrains/TSception

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TSception: A Deep Learning Framework for Emotion Detection Using EEG

https://github.com/desa-lab/eeg-image-reconstruction

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Perceptogram: Reconstructing Visual Percepts from EEG

https://github.com/devjake/eeg-diffusion-pytorch

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EEG Synthetic Data Generation Using Probabilistic Diffusion Models

https://github.com/dinuka-1999/braineocare

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Using Explainable AI for EEG-based Reduced Montage Neonatal Seizure Detection

https://github.com/dl4mhealth/comet

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Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series

https://github.com/dl4mhealth/lead

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LEAD: Large Foundation Model for EEG-Based Alzheimer's Disease Detection

https://github.com/dl4mhealth/medformer

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Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification

https://github.com/dlcjfgmlnasa/NeuroNet

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NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG

https://github.com/dlcjfgmlnasa/SynthSleepNet

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Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework

https://github.com/dominikklepl/AD-energy-landscape

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Characterising Alzheimer's Disease with EEG-based Energy Landscape Analysis

https://github.com/dreamento/dreamento

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Dreamento: an open-source dream engineering toolbox for sleep EEG wearables

https://github.com/drwuHUST/iGS

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Active Learning for Regression Using Greedy Sampling

https://github.com/dslaborg/frequency-pretraining

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Data-Efficient Sleep Staging with Synthetic Time Series Pretraining

https://github.com/dslaborg/sumo

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Advanced sleep spindle identification with neural networks

https://github.com/duanyiqun/ctnas-eeg

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Cross Task Neural Architecture Search for EEG Signal Classifications

https://github.com/dungscout96/DL-EEG

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Deep Convolutional Neural Network Applied to Electroencephalography: Raw Data vs Spectral Features

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.