YASA (Yet Another Spindle Algorithm): a Python package to analyze polysomnographic sleep recordings.

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

U-Time: A Fully Convolutional Network for Time Series Segmentation

Sleep As Android integration for Home Assistant

TinySleepNet: An Efficient Deep Learning Model for Sleep Stage Scoring based on Raw Single-Channel EEG by Akara Supratak and Yike Guo from The Faculty of ICT, Mahidol University and Imperial College London respectively

Dreamento (DReam ENgenieering TOolbox): a Python-based software for dream engineering while monitoring/analyzing real-time EEG data.

An open-source sleep stage classification Python package

Analyzing all my Quantified Self data

A lucid dreaming device that detects eye movements during REM sleep and triggers audio and visual alerts to promote lucidity. Like having your own private sleep lab technician that watches you sleep and alerts you when you are dreaming.

A toolbox for sleep data analysis and sleep stage classification

The world's first open-source bipolar & depression detector powered by your Apple Health data.

Luna software for sleep EEG analysis (base)

Sleep stage automatic classifier using CNNs and LSTMs to process frequency signals of brain activity recorded via ElectroEncephaloGram EEG. Undergraduate thesis

😴 DeepSleep2 is a compact U-Net-inspired convolutional neural network with 740,551 parameters, designed to predict non-apnea sleep arousals from full-length multi-channel polysomnographic recordings at 5-millisecond resolution. Achieves similar performance to DeepSleep with lower computational cost.

EEGLAB-compatible analysis software for manual / visual sleep stage scoring, signal processing and event marking of polysomnographic (PSG) data for MATLAB.

Linux and Mac OSX compatible script to pull CPAP Data from an Airsense 10/11 for review in Oscar/Sleep HQ.

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

A complete end-to-end project analyzing a synthetic sleep and lifestyle dataset. The project includes data cleaning, exploratory data analysis (visualizations), feature engineering, and machine learning modeling to predict sleep disorders. Built using Python, Pandas, Seaborn, Matplotlib, and Scikit-learn.