Biosignals Notebooks is an open collection of Jupyter Notebooks and a companion Python library designed to help you work with biosignals using PLUX systems such as BITalino and biosignalsplux. It provides practical, ready-to-use programming examples that guide you through the full workflow of recording, processing, and analyzing biosignals.
The notebooks cover key steps in the biosignals pipeline:
- loading and visualizing data,
- pre-processing single or multi-channel signals,
- detecting events,
- extracting features,
- training and evaluating machine-learning models.
Examples use a wide range of biosignals, including ECG, EMG, EDA, accelerometer, and respiration, and each notebook is labeled by topic, signal type, application area, and complexity level to help you quickly find what you need.
The included biosignalsnotebooks Python library provides the core toolbox used throughout the examples and can be installed via pip. Many notebooks also demonstrate how to integrate additional Python packages commonly used in biosignal processing.
Explore the full project on GitHub:
https://github.com/pluxbiosignals/biosignalsnotebooks
