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EEG

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

https://github.com/USC-InfoLab/NeuroGNN

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Dynamic GNNs for Precise Seizure Detection and Classification from EEG Data

https://github.com/Vesan-yws/NSSINet

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NSSI-Net: Multi-Concept Generative Adversarial Network for Non-Suicidal Self-Injury Detection Using High-Dimensional EEG Signals in a Semi-Supervised Learning Framework

https://github.com/VoiceBeer/MS-MDA

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MS-MDA: Multisource Marginal Distribution Adaptation for Cross-subject and Cross-session EEG Emotion Recognition

https://github.com/Westlake-AI/OpenBioSeq

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Neuro-BERT: Rethinking Masked Autoencoding for Self-supervised Neurological Pretraining

https://github.com/Xharlie/Eye-movement-similarity-clustering

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Report: Dynamic Eye Movement Matching and Visualization Tool in Neuro Gesture

https://github.com/XuanhaoLiu/mixEEG

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mixEEG: Enhancing EEG Federated Learning for Cross-subject EEG Classification with Tailored mixup

https://github.com/YYao-42/EEG-based_SVAD

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EEG-based Decoding of Selective Visual Attention in Superimposed Videos

https://github.com/YanhaoHuang23/FAT

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A novel Fourier Adjacency Transformer for advanced EEG emotion recognition

https://github.com/Yeganehfrh/EEGModalNet

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YARE-GAN: Yet Another Resting State EEG-GAN

https://github.com/YossiZhao/CwA-T

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CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality Detection

https://github.com/ZJU-BrainNet/BrainBenchmark

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Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics

https://github.com/ZhangXiao96/Speller-Attacks

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Tiny noise, big mistakes: Adversarial perturbations induce errors in Brain-Computer Interface spellers

https://github.com/ZihanLiu95/UAP_EEG

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Universal Adversarial Perturbations for CNN Classifiers in EEG-Based BCIs

https://github.com/aamixsh/eeg_mci

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On the challenges of detecting MCI using EEG in the wild

https://github.com/abbassalami/eeg-itnet

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EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classification

https://github.com/abhijitmishra/Thought2Text

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Thought2Text: Text Generation from EEG Signal using Large Language Models (LLMs)

https://github.com/acrophase/sleep_staging_kd

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A Deep Knowledge Distillation framework for EEG assisted enhancement of single-lead ECG based sleep staging

https://github.com/adam-zlatniczki/dimensional_causality

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Complete Inference of Causal Relations between Dynamical Systems

https://github.com/adam2392/morf-demo

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Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks

https://github.com/adrania/sleep-events-detection

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Multi-task deep-learning for sleep event detection and stage classification

https://github.com/agosztolai/rvgp

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Implicit Gaussian process representation of vector fields over arbitrary latent manifolds

https://github.com/aitrics/eeg_real_time_seizure_detection

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Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting

https://github.com/ajoshiusc/brainseg

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Learning from imperfect training data using a robust loss function: application to brain image segmentation

https://github.com/akaraspt/deepsleepnet

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DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG

https://github.com/akaraspt/tinysleepnet

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Quantifying the Impact of Data Characteristics on the Transferability of Sleep Stage Scoring Models

https://github.com/akaraspt/transferability_sleep

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Quantifying the Impact of Data Characteristics on the Transferability of Sleep Stage Scoring Models

https://github.com/akmeunier/eegchat

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A Conversational Brain-Artificial Intelligence Interface

https://github.com/albertcheng19/scatterformer

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ScatterFormer: Locally-Invariant Scattering Transformer for Patient-Independent Multispectral Detection of Epileptiform Discharges

https://github.com/alexander-malafeev/microsleep-detection

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Automatic detection of microsleep episodes with deep learning

https://github.com/alexanderbertrandlab/si-gcca

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Stimulus-Informed Generalized Canonical Correlation Analysis for Group Analysis of Neural Responses to Natural Stimuli

https://github.com/alidr79/cueless_eeg_subject_identification

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Cueless EEG imagined speech for subject identification: dataset and benchmarks

https://github.com/alihashemi-ai/dugh-neurips-2021

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Efficient Hierarchical Bayesian Inference for Spatio-temporal Regression Models in Neuroimaging

https://github.com/amch-q/csci6907project

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Effect of Kernel Size on CNN-Vision-Transformer-Based Gaze Prediction Using Electroencephalography Data

https://github.com/ammarmian/icml-rmt-2024

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Random matrix theory improved Fréchet mean of symmetric positive definite matrices

https://github.com/anaborovac/distributed-nsda

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Ensemble learning using individual neonatal data for seizure detection

https://github.com/anders-s-olsen/coupled-generator-decomposition

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Coupled generator decomposition for fusion of electro- and magnetoencephalography data

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.