.. EEGProc documentation master file, created by
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EEGProc documentation
=====================

.. toctree::
   :maxdepth: 2
   :caption: Contents:
   
   getting-started
   api/modules


EEGProc is a fully vectorized library designed for preprocessing and extracting features from EEG (Electroencephalogram) data. This library is optimized for performance and ease of use, making it suitable for researchers and developers working in the field of neuroscience, biomedical engineering, and machine learning.
Checkout and **star** or **fork** the project at https://github.com/VitorInserra/EEGProc

**Features**

- **Preprocessing**: Includes functions for filtering, artifact removal, and normalization of EEG signals.
- **Featurization**: Extracts meaningful features from EEG data, such as power spectral density, band power, and more.
- **Vectorized Operations**: Fully vectorized implementation ensures high performance and scalability for working with pandas dataframes.
- **Ease of Integration**: Designed to integrate seamlessly with existing Python workflows.

**Contributing**

Contributions are welcome! If you have ideas for new features or improvements, feel free to open an issue or submit a pull request.

**License**

This project is licensed under the GPLv2 License.