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/ncclabsustech/EEGdenoiseNet
공식 코드EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoising
https://github.com/ncclabsustech/Single-Channel-EEG-Denoise
공식 코드EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoising
https://github.com/ncclabsustech/deepseparator
공식 코드Embedding Decomposition for Artifacts Removal in EEG Signals
https://github.com/neerajwagh/eeg-gcnn
공식 코드https://github.com/neerajwagh/evaluating-eeg-representations
공식 코드https://github.com/neu-spiral/markovtype
공식 코드https://github.com/neurallabiitguwahati/e-asr
공식 코드Dynamical Embedding of Single Channel Electroencephalogram for Artifact Subspace Reconstruction
https://github.com/neurolib-dev/neurolib
공식 코드https://github.com/neuromat/eeg-tree-algorithms
공식 코드https://github.com/neuspeech/eeg-to-text
공식 코드https://github.com/neuspeech/mad-meg2text
공식 코드https://github.com/neuspeech/neugpt
공식 코드https://github.com/neuspeech/neuspeech1
공식 코드https://github.com/nicholascjl/imdb-lstm
공식 코드Self-Organization Towards $1/f$ Noise in Deep Neural Networks
https://github.com/nik-sm/bci-disc-models
공식 코드https://github.com/nithinparthasarthy/P300Speller
공식 코드Evaluation Of P300 Speller Performance Using Large Language Models Along With Cross-Subject Training
https://github.com/norrisjamie23/localising_soz_from_spes
공식 코드https://github.com/nzwang/neural-mcrl
공식 코드Neural-MCRL: Neural Multimodal Contrastive Representation Learning for EEG-based Visual Decoding
https://github.com/obada-alzoubi/appear
공식 코드Automated Pipeline for EEG Artifact Reduction (APPEAR) Recorded during fMRI
https://github.com/of2/cbma_paper_simulations
공식 코드https://github.com/of2/clusterbma
공식 코드https://github.com/okbalefthanded/pylpov
공식 코드https://github.com/okbalefthanded/stimusto
공식 코드https://github.com/osmanberke/Deep-SSVEP-BCI
공식 코드A Deep Neural Network for SSVEP-based Brain-Computer Interfaces
https://github.com/osmanberke/ensemble-of-dnns
공식 코드Transfer Learning of an Ensemble of DNNs for SSVEP BCI Spellers without User-Specific Training
https://github.com/otoolej/downsample_open_eeg
공식 코드Neonatal EEG graded for severity of background abnormalities in hypoxic-ischaemic encephalopathy
https://github.com/otoolej/ms_rocket
공식 코드Random Convolution Kernels with Multi-Scale Decomposition for Preterm EEG Inter-burst Detection
https://github.com/paolonapoletano/bci-resources
공식 코드On using AI for EEG-based BCI applications: problems, current challenges and future trends
https://github.com/patrickzib/motiflets
공식 코드Motiflets -- Simple and Accurate Detection of Motifs in Time Series
https://github.com/pbashivan/EEGLearn
공식 코드Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
https://github.com/pbiecek/challenges-xai-aging-aaai23
공식 코드Challenges facing the explainability of age prediction models: case study for two modalities
https://github.com/pbizopoulos/signal2image-modules-in-deep-neural-networks-for-eeg-classification
공식 코드Signal2Image Modules in Deep Neural Networks for EEG Classification
https://github.com/pedrorasb/fbcnn
공식 코드FBDNN: Filter Banks and Deep Neural Networks for Portable and Fast Brain-Computer Interfaces
https://github.com/percyzhai/fgm_neighborhood
공식 코드High-dimensional Functional Graphical Model Structure Learning via Neighborhood Selection Approach
https://github.com/philipph77/DAPE-Framework
공식 코드Domain-Invariant Representation Learning from EEG with Private Encoders
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.