Papers with Code 아카이브

EEG

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코드

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

https://github.com/Hyh200/Hyh200.github.io

공식 코드

Decoding visual brain representations from electroencephalography through Knowledge Distillation and latent diffusion models

https://github.com/IA3005/Elliptical-Wishart-for-Signal-Processing

공식 코드

Elliptical Wishart distributions: information geometry, maximum likelihood estimator, performance analysis and statistical learning

https://github.com/IS-UMK/supFunSim

공식 코드

Localization 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 코드

Calibration-Free Driver Drowsiness Classification based on Manifold-Level Augmentation

https://github.com/Kaist-ICLab/K-EmoCon_SupplementaryCodes

공식 코드

K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations

https://github.com/LorenaGallego/DL4ADpred

공식 코드

Alzheimer's disease detection in PSG signals

https://github.com/MDFahimAnjum/LightCNNforPD

공식 코드

Parkinson's Disease Classification via EEG: All You Need is a Single Convolutional Layer

https://github.com/MHersche/HDembedding-BCI

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Exploring Embedding Methods in Binary Hyperdimensional Computing: A Case Study for Motor-Imagery based Brain-Computer Interfaces

https://github.com/MSA-LMC/PhysioSync

공식 코드

PhysioSync: Temporal and Cross-Modal Contrastive Learning Inspired by Physiological Synchronization for EEG-Based Emotion Recognition

https://github.com/MathieuSeraphim/SPDTransNet_plus

공식 코드

Automatic Classification of Sleep Stages from EEG Signals Using Riemannian Metrics and Transformer Networks

https://github.com/Metaverse-Crowdsource/EEG-Chaos-Kuramoto-Neural-Net

공식 코드

Artificial Intelligence for EEG Prediction: Applied Chaos Theory

https://github.com/MiriamKirchhoff/BO_for_EEG-TMS_phase

공식 코드

Closed-Loop phase selection in EEG-TMS using Bayesian Optimization

https://github.com/Mithunjha/EarEEG_KnowledgeDistillation

공식 코드

A Knowledge Distillation Framework For Enhancing Ear-EEG Based Sleep Staging With Scalp-EEG Data

https://github.com/MultiScale-BCI/IV-2a

공식 코드

Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features

https://github.com/N-Nieto/Relevance_Based_Pruning

공식 코드

Extreme Learning Machine design for dealing with unrepresentative features

https://github.com/NeuroTechX/moabb

공식 코드

The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark

https://github.com/Niloy333/eegFloss

공식 코드

eegFloss: A Python package for refining sleep EEG recordings using machine learning models

https://github.com/Ori226/p300_lstm

공식 코드

Recurrent Neural Networks for P300-based BCI

https://github.com/PedroFerreiradaCosta/NeuroadaptiveEEG

공식 코드

Neuroadaptive electroencephalography: a proof-of-principle study in infants

https://github.com/PieraRiccio/Emotions-fromEEG-toArt

공식 코드

AI-based artistic representation of emotions from EEG signals: a discussion on fairness, inclusion, and aesthetics

https://github.com/Prog-Jacob/watermarking-eeg-models

공식 코드

Protecting Intellectual Property of EEG-based Neural Networks with Watermarking

https://github.com/RomGai/BrainVis

공식 코드

BrainVis: Exploring the Bridge between Brain and Visual Signals via Image Reconstruction

https://github.com/SPEECHCOG/PFML

공식 코드

PFML: Self-Supervised Learning of Time-Series Data Without Representation Collapse

https://github.com/SPOClab-ca/BENDR

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BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data

https://github.com/SajadMo/SleepEEGNet

공식 코드

SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach

https://github.com/SeyedMuhammadHosseinMousavi/Synthetic-Data-Generation-by-Supervised-Neural-Gas-Network

공식 코드

Synthetic Data Generation by Supervised Neural Gas Network for Physiological Emotion Recognition Data

https://github.com/ShiyaLiu/EEG-feature-filter-and-disguising

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Disguising Personal Identity Information in EEG Signals

https://github.com/SiavashShams/neuro2semantic

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Neuro2Semantic: A Transfer Learning Framework for Semantic Reconstruction of Continuous Language from Human Intracranial EEG

https://github.com/Singular-Brain/CCSPNet

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Towards Real-World BCI: CCSPNet, A Compact Subject-Independent Motor Imagery Framework

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

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GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals

https://github.com/Surayez/EmotivDriverDistraction

공식 코드

Classification of multivariate weakly-labelled time-series with attention

https://github.com/TSTEP99/tensor_decomp_eeg

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Tensor Decomposition of Large-scale Clinical EEGs Reveals Interpretable Patterns of Brain Physiology

https://github.com/TencentARC/ViT-Lens

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ViT-Lens: Towards Omni-modal Representations

https://github.com/TianwangJia/FedBS

공식 코드

Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces

https://github.com/Tolmez/DivFE

공식 코드

Classification of Motor Imagery EEG Signals by Using a Divergence Based Convolutional Neural Network

https://github.com/Tolmez/WalshDNN

공식 코드

Strengthening the Training of Convolutional Neural Networks By Using Walsh Matrix

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