A Python package for supporting the external loading and processing of OpenSignals electrophysiological acquisitions.
biosignalsnotebooks is a set of documents and a Python library to provide programming examples in the form of Jupyter Notebooks, as companion to the OpenSignals biosignals acquisition tools.
This collection of code samples has the purpose to help users of PLUX Wireless Biosignals systems, such as BITalino or biosignalsplux, and to the researcher or student interested on recording processing and classifying biosignals. The examples are set on a level of complexity to inspire the users and programmers on how easy some tasks are and that more complex ones can also be achieved, by reusing and recreating some of the examples presented here.
A Python library (entitled biosignalsnotebooks ) is the base toolbox to support the notebooks and to provide some useful functionalities. It can be installed through pip command, like demonstrated in a PyPI dedicated page.
In many cases we also point and illustrate with code the usage of other python toolboxes dedicated to biosignal processing.
The notebooks will cover the full topics pipeline of working with biosignals, such as: Load a file; Visualise the data online and offline, Pre-Process a one channel signal or a multi-channel acquisition, Detect relevant events in the signals, Extract features from many different type of sensors and domains, Train and Classify among a set of classes with several machine learning approaches, Understand the obtained results with metrics and validations techniques.
These examples are carried in a multitude of biosignals , from ECG, EDA, EMG, Accelerometer, Respiration among many others. The notebooks have a set of labels to help navigate among topics , types of signals , application area and complexity level to support the search for particular solutions.
We encourage you to share new example ideas, to pose questions email@example.com, and to make improvements or suggestion to this set of notebooks.
Be inspired on how to make the most of your biosignals!
What is PLUX
PLUX wireless biosignals is devoted to the creation innovative products for advanced biosignals monitoring platforms that integrate wearable body sensors combined with wireless connectivity, algorithms and software applications.
We have been perusing the mission of making biosignals as accessible as possible to researchers and students in many areas of application, ranging from biomedical engineering, computer science, human computer interaction, sport sciences, psychology, clinical research among other fields.
PLUX's Software and Hardware Environment
OpenSignals is the companion application to PLUX devices (BITalino or biosignalsplux) where the users collect visualize an process biosignals in a intuitive user interface. Opensignals is free and can be used also with signals collected from other devices.
In some cases OpenSignals provides plugins for advanced signals processing operations that automate some of the research process. Some of the plugins are curated and advanced versions of the base notebooks explained in here.
The list of plugins can be found here: http://biosignalsplux.com/en/software/add-ons
Access to biosignalsnotebooks Notebooks
For viewing biosignalsnotebooks .ipynb files correctly formatted and with the right CSS configurations the user should access the link contained in the previous image instead of navigating manually through the files in GitHub repository
Notebook Publication Status
Publication status is available in a Google Spreadsheet
Installation of biosignalsnotebooks package
In order to biosignalsnotebooks package be installed, the user should open a Windows command prompt (by searching for "cmd") and type the following instruction:
pip install biosignalsnotebooks
Release history Release notifications
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|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size biosignalsnotebooks-0.5.16-py3.7.egg (36.3 MB)||File type Egg||Python version 3.7||Upload date||Hashes View|
|Filename, size biosignalsnotebooks-0.5.16-py3-none-any.whl (36.1 MB)||File type Wheel||Python version py3||Upload date||Hashes View|
|Filename, size biosignalsnotebooks-0.5.16.tar.gz (35.9 MB)||File type Source||Python version None||Upload date||Hashes View|
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