Metadata-Version: 2.4
Name: bciflow
Version: 1.0.0.dev3
Summary: A package for BCI-related tasks, including dataset loading, preprocessing, feature extraction, and classification.
Project-URL: Homepage, https://github.com/Lab-HC/bciflow
Project-URL: Issues, https://github.com/Lab-HC/bciflow/issues
Project-URL: Documentation, https://lab-hc.github.io/bciflow/
Author-email: Gabriel Henrique de Souza <gabriel.souza@ufjf.br>, Hiero Henrique Barcelos Costa <hiero.costa@estudante.ufjf.br>, Marcelo de Melo Amorim Filho <marcelomelo.amorim@estudante.ufjf.br>
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Customer Service
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Telecommunications Industry
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.7
Requires-Dist: emd
Requires-Dist: h5py
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: pywavelets
Requires-Dist: scikit-learn
Requires-Dist: scipy
Description-Content-Type: text/markdown

# BCI Flow
Bciflow is a Python package focused on Brain-Computer Interface (BCI)-related work. It provides tools for loading pre-established datasets, performing analysis, pre-processing, filtering, feature extraction, and classification, covering the entire process of creating BCI models. Installation 

You can install bciflow directly from PyPI using pip: 

	pip install bciflow 

## Features 
- **Dataset Loading:** Support for popular BCI datasets, such as BCI Competition datasets, OpenBMI, among others. 
- **Pre-processing:** Filtering, artifact removal, normalization, and other data preparation techniques. 
- **Feature Extraction:** Methods for extracting relevant features from EEG signals, such as frequency bands, CSP (Common Spatial Patterns), etc. 
- **Classification:** Implementation of classification algorithms such as SVM, LDA, Neural Networks, among others. 
- **Complete Pipeline:** Facilitates the creation of complete pipelines for processing and analyzing BCI data.

## Loading a Dataset

The datasets must be loaded by the user, and the data path must be provided by the user in the path parameter. For example, to load the CBCIC dataset:

    from bciflow.datasets import cbcic
    # Load CBCIC dataset
    data = cbcic(
        subject=1,  # Subject number
        session_list=None,  # List of sessions to load (default: all sessions)
        labels=['left-hand', 'right-hand'],  # Labels to include
        path='data/cbcic/'  # Path to the dataset files
    )