[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

Official repository of cross-modal transformer for interpretable automatic sleep stage classification. https://arxiv.org/abs/2208.06991

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

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"

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

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

Towards Domain Free Transformer for Generalized EEG Pre-training

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

MATLAB Project to Classify Different Sleep Stages of the EEG Signals using Machine Learning (Random Forest and Support Vector Machine)

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

This project focuses on the classification of a subjects sleep stage based on their Apple Watch data

Automated Sleep Stage Scoring using Deep Learing

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

Codes related to the paper "Attention-Based CNN-BiLSTM for Sleep States Classification of Spatiotemporal Wide-Field Calcium Imaging Data"

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

MATLAB codes for reproducing the paper "A New Post-Processing Method Using Latent Structure Influence Models for Channel Fusion in Automatic Sleep Staging"