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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/ByeonghooLee-ku/ICASSP2020-2020-code

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Classification of High-Dimensional Motor Imagery Tasks based on An End-to-end role assigned convolutional neural network

https://github.com/CECNL/ExBrainable

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ExBrainable: An Open-Source GUI for CNN-based EEG Decoding and Model Interpretation

https://github.com/DashanGao/Federated-Transfer-Learning-for-EEG

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Federated Transfer Learning for EEG Signal Classification

https://github.com/GeometricBCI/Tensor-CSPNet-and-Graph-CSPNet

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Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification

https://github.com/HAIx-Lab/SpatialExplanations4BCI

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Quantifying Spatial Domain Explanations in BCI using Earth Mover's Distance

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/MultiScale-BCI/IV-2a

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

https://github.com/NeuroTechX/moabb

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

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/TianwangJia/FedBS

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

https://github.com/Tolmez/DivFE

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

https://github.com/abbassalami/eeg-itnet

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EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classification

https://github.com/bafanas/dim-reduction-with-cnn-lstm

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Applying Dimensionality Reduction as Precursor to LSTM-CNN Models for Classifying Imagery and Motor Signals in ECoG-Based BCIs

https://github.com/carraraig/augmented_covariance_bci

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Classification of BCI-EEG based on augmented covariance matrix

https://github.com/cecnl/eeg-kd

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Enhancing Low-Density EEG-Based Brain-Computer Interfaces with Similarity-Keeping Knowledge Distillation

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

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Cross Task Neural Architecture Search for EEG Signal Classifications

https://github.com/eeg-augmentation-benchmark/eeg-augmentation-benchmark-2022

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Data augmentation for learning predictive models on EEG: a systematic comparison

https://github.com/elouayas/eegsimpleconv

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A Strong and Simple Deep Learning Baseline for BCI MI Decoding

https://github.com/galvinlim/agtcnet

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AGTCNet: A Graph-Temporal Approach for Principled Motor Imagery EEG Classification

https://github.com/gparisa/eegbciMI

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Comparison of Classification Algorithms Towards Subject-Specific and Subject-Independent BCI

https://github.com/guangyizhangbci/eeg_riemannian

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Spatio-Temporal EEG Representation Learning on Riemannian Manifold and Euclidean Space

https://github.com/gzoumpourlis/ensemble-mi

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Motor Imagery Decoding Using Ensemble Curriculum Learning and Collaborative Training

https://github.com/hehe91/LA

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Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment Approach

https://github.com/ibm/eeg-ieeg-brain-compressor

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The Case for Cleaner Biosignals: High-fidelity Neural Compressor Enables Transfer from Cleaner iEEG to Noisier EEG

https://github.com/iis-eth-zurich/eeg-tcnet

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EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces

https://github.com/inria-nerv/happyfeat

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HappyFeat -- An interactive and efficient BCI framework for clinical applications

https://github.com/iobt-vistec/min2net

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MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification

https://github.com/jesus-333/Dynamic-PyTorch-Net

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CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From The State-of-The-Art to DynamicNet

https://github.com/jesus-333/FBCSP-Python

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CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From The State-of-The-Art to DynamicNet

https://github.com/julianagonzalezastudillo/netfeat

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Feature interpretability in BCIs: exploring the role of network lateralization

https://github.com/kolcs/bionic_apps

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Deep comparisons of Neural Networks from the EEGNet family

https://github.com/martinwimpff/channel-attention

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EEG motor imagery decoding: A framework for comparative analysis with channel attention mechanisms

https://github.com/martinwimpff/eeg-continual

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Fine-Tuning Strategies for Continual Online EEG Motor Imagery Decoding: Insights from a Large-Scale Longitudinal Study

https://github.com/martinwimpff/eeg-otta

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Calibration-free online test-time adaptation for electroencephalography motor imagery decoding

https://github.com/max-phairot-a/mixnet

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MixNet: Joining Force of Classical and Modern Approaches Toward the Comprehensive Pipeline in Motor Imagery EEG Classification

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