Papers with Code 아카이브

Brain Decoding

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 1
평가표: 2
보존된 평가 기록: 8

Motor Brain Decoding is fundamental task for building motor brain computer interfaces (BCI).

Progress in predicting finger movements based on brain activity allows us to restore motor functions and improve rehabilitation process of patients.

코드

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

https://bitbucket.org/lvareka/cnnforgtn

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Evaluation of convolutional neural networks using a large multi-subject P300 dataset

https://github.com/AlexOlza/DA-enhanced-searchlight

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Domain Adaptation-Enhanced Searchlight: Enabling classification of brain states from visual perception to mental imagery

https://github.com/BTajini/augfmri

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Functional Magnetic Resonance Imaging data augmentation through conditional ICA

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/Dingkun0817/SDDA

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Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification

https://github.com/Dntfreitas/GA_PSO_DEEP_LEARNING

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Multiple Time Series Fusion Based on LSTM An Application to CAP A Phase Classification Using EEG

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/Ha0Tang/SelectionGAN

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Multi-Channel Attention Selection GANs for Guided Image-to-Image Translation

https://github.com/HerrmannM/paper-2021-EAPElasticDist

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Early Abandoning and Pruning for Elastic Distances including Dynamic Time Warping

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

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

https://github.com/KDongYoung/Calibration-Free-Driver-Drowsiness-Classification-based-on-Manifold-Level-Augmentation

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

https://github.com/KamitaniLab/GOD_stimuli_annotations

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Spurious reconstruction from brain activity

https://github.com/KangchengWu/OIQA

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Viewport-Unaware Blind Omnidirectional Image Quality Assessment: A Flexible and Effective Paradigm

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/MaryamOstadsharif/RISE-iEEG

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RISE-iEEG: Robust to Inter-Subject Electrodes Implantation Variability iEEG Classifier

https://github.com/MonashTS/tempo

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Early Abandoning and Pruning for Elastic Distances including Dynamic Time Warping

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/Nuctech-AI/LBS_pruning

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Towards Optimal Filter Pruning with Balanced Performance and Pruning Speed

https://github.com/Ori226/p300_lstm

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Recurrent Neural Networks for P300-based BCI

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

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

https://github.com/RemilYoucef/split-sd4x

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Interpretable Summaries of Black Box Incident Triaging with Subgroup Discovery

https://github.com/SCUT-AILab/DCP

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Discrimination-aware Channel Pruning for Deep Neural Networks

https://github.com/SMehrkanoon/Deep-brain-state-classification-of-MEG-data

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Deep brain state classification of MEG data

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/Singular-Brain/CCSPNet

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

https://github.com/Soumyabrata/rough-sets

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Rough Set Based Color Channel Selection

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/WeizmannVision/SelfSuperReconst

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More Than Meets the Eye: Self-Supervised Depth Reconstruction From Brain Activity

https://github.com/XU-TIANYANG/GFS-DCF

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Joint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking

https://github.com/YulongBonjour/BrainCLIP

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BrainCLIP: Bridging Brain and Visual-Linguistic Representation Via CLIP for Generic Natural Visual Stimulus Decoding

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