Papers with Code 아카이브
Electroencephalogram (EEG)
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/sislam99/fmri_state_transition_dynamics
공식 코드https://github.com/sonycslparis/audioic
공식 코드https://github.com/soupeeli/NeuroBOLT
공식 코드NeuroBOLT: Resting-state EEG-to-fMRI Synthesis with Multi-dimensional Feature Mapping
https://github.com/spiresearch/sacd
공식 코드Seeing your sleep stage: cross-modal distillation from EEG to infrared video
https://github.com/spm/spm
공식 코드BSD: a Bayesian framework for parametric models of neural spectra
https://github.com/sremes/wishart-gibbs-kernel
공식 코드A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings
https://github.com/svaneynd/structured-cmtf
공식 코드https://github.com/sweichwald/MERLiN
공식 코드https://github.com/sweichwald/coroICA-python
공식 코드Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise
https://github.com/sylvchev/mdla
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/t0nyliang/EEGMobile
공식 코드https://github.com/tcs211/ai_eeeg_report
공식 코드A Hybrid Artificial Intelligence System for Automated EEG Background Analysis and Report Generation
https://github.com/theabrusch/flextime
공식 코드https://github.com/theabrusch/multiview_ts_ssl
공식 코드Multi-view self-supervised learning for multivariate variable-channel time series
https://github.com/theyou21/BigProject
공식 코드Mental Task Classification Using Electroencephalogram Signal
https://github.com/thijor/pyntbci
공식 코드https://github.com/thinknew/enk
공식 코드https://github.com/tidiane-camaret/eegclip
공식 코드EEG-CLIP : Learning EEG representations from natural language descriptions
https://github.com/tiehangd/MUPS
공식 코드Ultra Efficient Transfer Learning with Meta Update for Cross Subject EEG Classification
https://github.com/tiehangd/ue-eeg
공식 코드Uncertainty Detection and Reduction in Neural Decoding of EEG Signals
https://github.com/tjunlp-lab/CogAlign
공식 코드CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing Signals
https://github.com/tonahdztoro/Toward_asynchronous
공식 코드Toward asynchronous EEG-based BCI: Detecting imagined words segments in continuous EEG signals
https://github.com/treder/Fast-Least-Squares
공식 코드Cross-validation in high-dimensional spaces: a lifeline for least-squares models and multi-class LDA
https://github.com/tsy935/eeg-gnn-ssl
공식 코드Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis
https://github.com/ufal/eyetracked-multi-modal-translation
공식 코드https://github.com/uros-s/diffattractreconstruct
공식 코드Efficient and faithful reconstruction of dynamical attractors using homogeneous differentiators
https://github.com/vdelv/emotion-eeg
공식 코드A Saliency based Feature Fusion Model for EEG Emotion Estimation
https://github.com/vishaln15/NeonatalSeizureDetection
공식 코드Scalable Machine Learning Architecture for Neonatal Seizure Detection on Ultra-Edge Devices
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/western-oc2-lab/signal-processing-for-machine-learning
공식 코드On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach
https://github.com/why135724/SWPC
공식 코드Motor Imagery Classification for Asynchronous EEG-Based Brain-Computer Interfaces
https://github.com/williamro/eeg-denoise
공식 코드DTP-Net: Learning to Reconstruct EEG signals in Time-Frequency Domain by Multi-scale Feature Reuse
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.