sleep-edf

CCRRSleepNet: A Hybrid Relational Inductive Biases Network for Automatic Sleep Stage Classification on Raw Single-Channel EEG

EEG & Hypnogram Sleep Quality Scoring with CNN + LSTM

An end-to-end deep learning pipeline for automatic sleep stage classification from polysomnography (PSG) signals. The system classifies 30-second EEG/EOG/EMG epochs into 5 AASM sleep stages (Wake, N1, N2, N3, REM) using a dual-input Teacher model (CNN + Transformer, κ=0.636) distilled into a lightweight Student model

classification and dim reduction methods with traditional machine learning techniques

Clasificación de estadios de sueño usando EEG, EOG y EMG del dataset Sleep-EDF Expanded // Sleep stage classification using EEG, EOG, and EMG from the Sleep-EDF Expanded dataset

Closed-loop BCI pipeline for automated lucid dream induction via real-time EEG sleep stage classification | Part of LUCID: Reality?

Sleep stage classification from raw EEG/EOG using a spatial-temporal CNN (Chambon 2018 variant). Trained on PhysioNet SleepEDF-78 with MNE-Python preprocessing, ICA artifact removal, and PyTorch. Achieves ~0.72 Cohen's Kappa on subject-wise held-out test set.

Interpretable EEG sleep-stage classification (Sleep-EDF) with subject-wise cross-validation and HMM temporal smoothing.

Exploratory sleep staging and fragmentation analysis from Sleep-EDF PSG data

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.