EEGProc documentation

Contents:

• Getting Started
  • Installation
  • Dependencies
  • Quick Start
  • Documentation Structure
• eegproc
  • eegproc package

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.