There are 8 repositories under biosignals topic.
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
BrainFlow is a library intended to obtain, parse and analyze EEG, EMG, ECG and other kinds of data from biosensors
DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
A Python Toolbox for Statistics and Neurophysiological Signal Processing (EEG, EDA, ECG, EMG...).
CS198-96: Intro to Neurotechnology @ UC Berkeley
SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach
EntroPy: complexity of time-series in Python (DEPRECATED)
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
Corpus of resources for multimodal machine learning with physiological signals (mmps).
Biosignal Processing in Python
Systole: A python package for cardiac signal synchrony and analysis
A graphical user interface for feature extraction from heart- and breathing biosignals.
Python API for Mentalab biosignal aquisition devices
Get stress measurement results in your IOS app using Welltory heart rate variability algorithms
Documentation for Reproducing & Using the OpenBCI cEEGrid Adapter
A Python package for the analysis of biopsychological data.
Interpretable Pre-Trained Transformers for Heart Time-Series Data
Making OpenBCI for Node Reactive
This is an official implementation for "Intra- and inter-epoch temporal context network (IITNet) using sub-epoch features for automatic sleep scoring on raw single-channel EEG".
Preprocessing and classify EMG signals, using Tensorflow and Tensorflow Lite to deploy an AI model in a ESP32C3
PhysioKit: Open-source, accessible Physiological Computing Toolkit [Sensors 2023]
Documentation for Reproducing & Using the Open ExG Headphones
Resources for the paper titled "Domain-guided Self-supervision of EEG Data Improves Downstream Classification Performance and Generalizability". Accepted at ML4H Symposium 2021 with an oral spotlight!
Source code for multiple parameter modelling of synthetic electromyography data.
BioDG is a publically available framework for the evaluation of Domain Generalization algorithms in Biosignal Classification.
Pipeline and Dataset helpers for complex algorithm evaluation.
(WSDM'24) Cross-modal Self-Supervised Learning for Time-series through Latent Masking
Official repository for "Blind Source Separation of Single-Channel Mixtures via Multi-Encoder Autoencoders".
[arxiv 2024] ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling
Listen2YourHeart is a publically available, extendable framework for training Neural Networks via Contrastive SSL learning, for Phonocardiogram classification.