https://github.com/mccorsi/fucone
공식 코드Functional connectivity ensemble method to enhance BCI performance (FUCONE)
Papers with Code 아카이브
Motor Imagery
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Classification of examples recorded under the Motor Imagery paradigm, as part of Brain-Computer Interfaces (BCI).
A number of motor imagery datasets can be downloaded using the MOABB library: motor imagery datasets list
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mcd4874/neurips_competition
공식 코드https://github.com/medmaxlab/eegprepro
공식 코드The more, the better? Evaluating the role of EEG preprocessing for deep learning applications
https://github.com/miaozhengqing/lmda-code
공식 코드https://github.com/miaozhengqing/tsff
공식 코드Time-space-frequency feature Fusion for 3-channel motor imagery classification
https://github.com/p-manivannan/uq-motor-imagery
공식 코드Uncertainty Quantification for cross-subject Motor Imagery classification
https://github.com/pulp-platform/q-eegnet
공식 코드https://github.com/qinxinlan/eeg-experiment-to-understand-differences-in-blinking
공식 코드https://github.com/ravikiran-mane/FBCNet
공식 코드FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interface
https://github.com/sionan/miccai2024-restl
공식 코드https://github.com/sylvchev/wcci-rgcon
공식 코드https://github.com/sylyoung/DeepTransferEEG
공식 코드T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs
https://github.com/tiehangd/ue-eeg
공식 코드Uncertainty Detection and Reduction in Neural Decoding of EEG Signals
https://github.com/vlawhern/arl-eegmodels
공식 코드EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
https://github.com/wenhui0206/neurogpt
공식 코드https://github.com/why135724/SWPC
공식 코드Motor Imagery Classification for Asynchronous EEG-Based Brain-Computer Interfaces
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/xc-zhonghit/eeg-dg
공식 코드EEG-DG: A Multi-Source Domain Generalization Framework for Motor Imagery EEG Classification
https://github.com/xqchen914/abat
공식 코드https://gitlab.com/pierregtch/motor_embedding_benchmark
공식 코드https://github.com/Aadhvin02/https-github.com-meagmohit-EEG-Datasets
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
https://github.com/Amir-Hofo/EEGNet
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
https://github.com/Amir-Hofo/EEGNet_Pytorch
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
https://github.com/Dekakhrone/EEGNet
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
https://github.com/JiajZhu/EEGnet_Pytorch
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
https://github.com/Kanyooo/EEG-DCNet
EEG-DCNet: A Fast and Accurate MI-EEG Dilated CNN Classification Method
https://github.com/LIKANblk/AML_EEG_challenge
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
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/RuiLiu-cc/FLEEG
Aggregating Intrinsic Information to Enhance BCI Performance through Federated Learning
https://github.com/YundongWang/BCI_Challenge
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
https://github.com/adwaykanhere/FYP
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
https://github.com/amrzhd/EEGNet
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
https://github.com/drwuHUST/TLBCI
Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipeline
https://github.com/emotionlab/eegain
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.