https://github.com/USC-InfoLab/NeuroGNN
공식 코드Dynamic GNNs for Precise Seizure Detection and Classification from EEG Data
Papers with Code 아카이브
Electroencephalogram (EEG)
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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/Vesan-yws/NSSINet
공식 코드https://github.com/VoiceBeer/MS-MDA
공식 코드https://github.com/Westlake-AI/OpenBioSeq
공식 코드Neuro-BERT: Rethinking Masked Autoencoding for Self-supervised Neurological Pretraining
https://github.com/Xharlie/Eye-movement-similarity-clustering
공식 코드Report: Dynamic Eye Movement Matching and Visualization Tool in Neuro Gesture
https://github.com/XuanhaoLiu/mixEEG
공식 코드mixEEG: Enhancing EEG Federated Learning for Cross-subject EEG Classification with Tailored mixup
https://github.com/YYao-42/EEG-based_SVAD
공식 코드EEG-based Decoding of Selective Visual Attention in Superimposed Videos
https://github.com/YanhaoHuang23/FAT
공식 코드A novel Fourier Adjacency Transformer for advanced EEG emotion recognition
https://github.com/Yeganehfrh/EEGModalNet
공식 코드https://github.com/YossiZhao/CwA-T
공식 코드CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality Detection
https://github.com/YoungJooHan/NM-FlowGAN
공식 코드https://github.com/ZJU-BrainNet/BrainBenchmark
공식 코드Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics
https://github.com/ZhangXiao96/Speller-Attacks
공식 코드https://github.com/ZihanLiu95/UAP_EEG
공식 코드Universal Adversarial Perturbations for CNN Classifiers in EEG-Based BCIs
https://github.com/aamixsh/eeg_mci
공식 코드https://github.com/abbassalami/eeg-itnet
공식 코드EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classification
https://github.com/abhijitmishra/Thought2Text
공식 코드Thought2Text: Text Generation from EEG Signal using Large Language Models (LLMs)
https://github.com/acrophase/sleep_staging_kd
공식 코드https://github.com/adam-zlatniczki/dimensional_causality
공식 코드Complete Inference of Causal Relations between Dynamical Systems
https://github.com/adam2392/morf-demo
공식 코드Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks
https://github.com/adrania/sleep-events-detection
공식 코드Multi-task deep-learning for sleep event detection and stage classification
https://github.com/agosztolai/rvgp
공식 코드Implicit Gaussian process representation of vector fields over arbitrary latent manifolds
https://github.com/aitrics/eeg_real_time_seizure_detection
공식 코드https://github.com/ajoshiusc/brainseg
공식 코드https://github.com/akaraspt/deepsleepnet
공식 코드DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
https://github.com/akaraspt/tinysleepnet
공식 코드Quantifying the Impact of Data Characteristics on the Transferability of Sleep Stage Scoring Models
https://github.com/akaraspt/transferability_sleep
공식 코드Quantifying the Impact of Data Characteristics on the Transferability of Sleep Stage Scoring Models
https://github.com/akmeunier/eegchat
공식 코드https://github.com/albertcheng19/scatterformer
공식 코드https://github.com/alexander-malafeev/microsleep-detection
공식 코드Automatic detection of microsleep episodes with deep learning
https://github.com/alexanderbertrandlab/si-gcca
공식 코드https://github.com/alidr79/cueless_eeg_subject_identification
공식 코드Cueless EEG imagined speech for subject identification: dataset and benchmarks
https://github.com/alihashemi-ai/dugh-neurips-2021
공식 코드Efficient Hierarchical Bayesian Inference for Spatio-temporal Regression Models in Neuroimaging
https://github.com/amch-q/csci6907project
공식 코드https://github.com/ammarmian/icml-rmt-2024
공식 코드Random matrix theory improved Fréchet mean of symmetric positive definite matrices
https://github.com/anaborovac/distributed-nsda
공식 코드Ensemble learning using individual neonatal data for seizure detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.