Creativity in Vitro EEG Analysis

This repository contains code and data for analyzing EEG (Electroencephalography) data related to Creativity in vitro studies. The project uses Python-based tools for EEG data processing and analysis.

Project Structure

.
├── bids_dataset/     # BIDS-formatted EEG dataset
├── data/
├── notebooks/        # Jupyter notebooks for analysis
└── environment.yml   # Conda environment configuration

Setup

This project uses Conda for environment management. To set up the environment:

1. Install Miniconda or Anaconda
2. Create the environment:

   conda env create -f environment.yml

3. Activate the environment:

   conda activate creativity-eeg

Dependencies

The project uses the following main Python packages:

• Python 3.10
• pandas: Data manipulation and analysis
• numpy: Numerical computing
• matplotlib & seaborn: Data visualization
• scipy: Scientific computing
• jupyterlab: Interactive development environment
• mne: EEG/MEG data processing
• scikit-learn: Machine learning tools

Usage

1. Activate the conda environment
2. Launch Jupyter Lab:

   jupyter lab

3. Navigate to the notebooks/ directory to access the analysis notebooks

Data

The project contains raw EEG data (first_dataset_raw) recorded using the OpenBCI Cyton board with the Daisy extension (14 channels total). The recordings were made as part of the project Creativity in vitro, which investigates how brain activity responds to lyrical and semantic patterns in music.

Lina, the participant listened to 20 repeated loops of 3 of the first six phrases of the song Aquarela by Toquinho. These 20 sessions were recorded consecutively in one continuous session, without restarting the OpenBCI GUI.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contributing

Contributions are welcome! To contribute:

1. Fork this repository.
2. Create a new branch for your feature or bugfix.
3. Make your changes and commit them with clear messages.
4. Push your branch to your fork.
5. Open a pull request describing your changes.

Please ensure your code is well-documented and follows best practices. If you are contributing code, try to include tests or example usage when possible. For questions or suggestions, feel free to open an issue.