sleep-stage-classification

[IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"

[TNSRE 2021] "An Attention-based Deep Learning Approach for Sleep Stage Classification with Single-Channel EEG"

[TKDD 2023] AdaTime: A Benchmarking Suite for Domain Adaptation on Time Series Data

An open-source sleep stage classification Python package

[IEEE TNNLS] NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG

[IEEE JBHI] "MultiChannelSleepNet: A Transformer-Based Model for Automatic Sleep Stage Classification With PSG"

[TNSRE 2023] Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation

Meta-Learning for EEG, Sleep Staging, Transfer Learning, Pre-trained EEG, PSG datasets (IEEE Journal of Biomedical and Health Informatics)

[IEEE TETCI] "ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training"

Official code for "SimPSI: A Simple Strategy to Preserve Spectral Information in Time Series Data Augmentation", AAAI 2024.

Towards Domain Free Transformer for Generalized EEG Pre-training

A Novel Adversarial Approach for EEG Dataset Refinement: Enhancing Generalization through Proximity-to-Boundary Scoring

Codes related to paper "Automated sleep stage classification of wide-field calcium imaging data via multiplex visibility graphs and deep learning"

This is a simple framework to preprocess and classify sleep stages based on recorded EEG/EMG data

Automated Sleep Stage Scoring using Deep Learing

A Multi-Level Tree-based Ensemble Model for Automatic Sleep Stage Classification

[Arxiv] NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG

Multi-channel vision Transformer-based automated sleep staging for EEG monitoring

TempoSleep is a context-aware framework for automatic single-channel EEG sleep staging. It combines multi-scale temporal feature extraction with hierarchical temporal modeling to capture local and long-range dependencies, with particular emphasis on N1-stage recognition.