Skip to main content

Package for HappyFeat

Project description

HappyFeat - Interactive framework for clinical BCI applications

docs status test: status PyPI version License: BSD-3

HappyFeat is a software aiming to to simplify the use of BCI pipelines in clinical settings. More precisely, it is a software assitant for extracting and selecting classification features for BCI.

It gathers all necessary manipulations and analysis in a single convenient GUI, and automates experimental or analytic parameters. The resulting workflow allows for effortlessly selecting the best features, helping to achieve good BCI performance in time-constrained environments. Alternative features based on Functional Connectivity can be used and compared or combined with Power Spectral Density, allowing a network-oriented approach.

It consists of Qt-based GUIs and Python toolboxes, allowing to realize all steps for customizing and fine-tuning a BCI system: feature extraction & selection, classifier training.

HappyFeat also allows to interface with BCI softwares (OpenViBE for the moment!) in order to facilitate the whole BCI workflow, from data acquisition to online classification.

The focus is put on ease of use, trial-and-error training of the classifier, and fast and efficient analysis of features of interest from BCI sessions.

Key Features

  • Easy to use GUI allowing to extract and visualize classification features, and select the most relevant ones for training a classifier.
  • Use Spectral Power or Coherence-based features for classification. HappyFeat allows to extract & visualize both types of features in parallel, and mix them at the training level.
  • Feature selection and classifier training can be done multiple times in a row, until satisfactory results are achieved.
  • A worspace management system keeps tracks of all extraction- and training-related manipulations, and enables a high degree of reproducibility.

Requirements

  • Python 3.9 or more recent
  • Python packages : shutils / PySide2 / numpy / MNE / matplotlib / scipy / spectrum / statsmodel / pandas
  • OpenViBE Version 3.6.0: http://openvibe.inria.fr/downloads/

Installation & Full documentation

HappyFeat is available as a package on PyPi. Otherwise, you can clone this repository.

Go to https://happyfeat.readthedocs.io/en/latest/ for more details.

License

This software is licensed using BSD 3-Clause. Please refer to LICENSE.md for more details.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

happyfeat-0.2.2.tar.gz (1.4 MB view details)

Uploaded Source

Built Distribution

happyfeat-0.2.2-py3-none-any.whl (266.8 kB view details)

Uploaded Python 3

File details

Details for the file happyfeat-0.2.2.tar.gz.

File metadata

  • Download URL: happyfeat-0.2.2.tar.gz
  • Upload date:
  • Size: 1.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.0 CPython/3.9.6

File hashes

Hashes for happyfeat-0.2.2.tar.gz
Algorithm Hash digest
SHA256 a735f06b16c35c62d2bbcb3830f9daeff266d2ea7f072d8d968b23d1881c42b6
MD5 cfe4a3b51ff234afd521beee437ca28f
BLAKE2b-256 081ebc8c7f3d1a09864a0014e317265854c885cd5a987a65a79653ba785d0ee1

See more details on using hashes here.

File details

Details for the file happyfeat-0.2.2-py3-none-any.whl.

File metadata

  • Download URL: happyfeat-0.2.2-py3-none-any.whl
  • Upload date:
  • Size: 266.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.0 CPython/3.9.6

File hashes

Hashes for happyfeat-0.2.2-py3-none-any.whl
Algorithm Hash digest
SHA256 21bdc5a4b8576ec389fdaf0558313d4d5f8bd8544ec65172bd37cdd9838b1b17
MD5 1ab4f0d20f4ea0455436dfe78dc9026e
BLAKE2b-256 256b2c49bef9ef77ef6a34d42dc6f3201f75876749b6ae826cd08dccae78c638

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page