https://github.com/akhilmurali013/project
Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs
Papers with Code 아카이브
Electroencephalogram (EEG)
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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Electroencephalogram (EEG) is a method of recording brain activity using electrophysiological indexes. When the brain is active, a large number of postsynaptic potentials generated synchronously by neurons are formed after summation. It records the changes of electric waves during brain activity and is the overall reflection of the electrophysiological activities of brain nerve cells on the surface of cerebral cortex or scalp. Brain waves originate from the postsynaptic potential of the apical dendrites of pyramidal cells. The formation of synchronous rhythm of EEG is also related to the activity of nonspecific projection system of cortex and thalamus. EEG is the basic theoretical research of brain science. EEG monitoring is widely used in its clinical application.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/alphacsc/alphacsc
Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals
https://github.com/amrzhd/EEGNet
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
https://github.com/andrire/RNNASIP-pulpissimo
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
https://github.com/beaupranisaa/EEGImageReconstruction
https://github.com/bluesky314/Cyclical_LR_Scheduler_With_Decay_Pytorch
https://github.com/brainhack-school2020/BHS-AuditoryMultimodal
https://github.com/buptlwz/mabp
https://github.com/chippybautista/zuco-sentiment-analysis
https://github.com/cran/RFCCA
Conditional canonical correlation estimation based on covariates with random forests
https://github.com/cyrille-feu/Discrete-Wavelet-Transform-Features-in-the-Prediction-of-Epileptic-Seizures
https://github.com/danielemarinazzo/GT_trous
Multiscale Granger causality analysis by à trous wavelet transform
https://github.com/dl4mhealth/adformer
ADformer: A Multi-Granularity Transformer for EEG-Based Alzheimer's Disease Assessment
https://github.com/dl4mhealth/medts_evaluation
Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification
https://github.com/dmlc/dgl/tree/master/examples/pytorch/eeg-gcnn
https://github.com/dongyangli-del/eeg_image_decode
https://github.com/drwuHUST/TLBCI
Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipeline
https://github.com/duggalrahul/REST
REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild
https://github.com/eeyhsong/eeg-transformer
Transformer-based Spatial-Temporal Feature Learning for EEG Decoding
https://github.com/emmanuelkalunga/Offline-Riemannian-SSVEP
Using Riemannian geometry for SSVEP-based Brain Computer Interface
https://github.com/esantamariavazquez/EEG-Inception
https://github.com/ethorsrud/Master
EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals
https://github.com/exporl/auditory-eeg-challenge-2023-code
Extracting Different Levels of Speech Information from EEG Using an LSTM-Based Model
https://github.com/famousgrouse/AccSleepNet
DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
https://github.com/gitzh-chen/rmlr
Riemannian Multinomial Logistics Regression for SPD Neural Networks
https://github.com/gmarceca/UTime-PlasmaStates
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging
https://github.com/gurucharanmk/PyTorch_CosineAnnealingWithRestartsLR
https://github.com/hubertjb/dl-eeg-review
Deep learning-based electroencephalography analysis: a systematic review
https://github.com/jaamiila/IntroSe-ales
https://github.com/jae1213/CyclicalLR_SGDR
https://github.com/jhupiterz/SafeSeizure
https://github.com/jinglescode/python-signal-processing
https://github.com/jinglescode/ssvep-multi-task-learning
https://github.com/jolibrain/caffe
https://github.com/jstranne/mouse_self_supervision
Uncovering the structure of clinical EEG signals with self-supervised learning
https://github.com/kamalravi/Imagined-Speech-Classification-using-EEG-
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.