CompNeuro

EEG/MEG Data Pre-processing and Analysis

Overview

This project involves the pre-processing and analysis of EEG/MEG data. The primary objectives are to clean, transform, and analyze the data to extract meaningful insights and patterns. The project utilizes various machine learning and statistical techniques to enhance the quality of the data and perform robust analysis.

Features

• Data Pre-processing: Cleaning and transforming raw EEG/MEG data.
• Feature Extraction: Identifying significant features from the data.
• Data Visualization: Visualizing the data for better understanding and interpretation.
• Machine Learning Models: Applying machine learning algorithms to analyze the data.
• Statistical Analysis: Conducting various statistical tests and models to validate findings.

Prerequisites

Before you begin, ensure you have the following:

• Python 3.7 or higher
• Jupyter Notebook
• Libraries: NumPy, Pandas, SciPy, Scikit-Learn, MNE, Matplotlib, Seaborn

Installation

1. Clone the repository:

   git clone https://github.com/tinopenchev/EEG-MEG-Data-Preprocessing-and-Analysis.git

2. Navigate to the project directory:

   cd EEG-MEG-Data-Preprocessing-and-Analysis

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

To run the project, open the EEG:MEG data pre-processing and analysis.ipynb file in Jupyter Notebook:

jupyter notebook EEG:MEG data pre-processing and analysis.ipynb

Follow the instructions within the notebook to pre-process and analyze your EEG/MEG data.

Running Tests

To run tests on the pre-processing and analysis steps, execute the following command:

pytest

Ensure that the test scripts are placed in the appropriate directory as specified in the project structure.

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature/your-feature-name

3. Make your changes.
4. Commit your changes:

   git commit -m 'Add your feature'

5. Push to the branch:

   git push origin feature/your-feature-name

6. Create a pull request.

License

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

Contact

For any questions, please contact:

• Tino Penchev
• Email: tinopenchev@gmail.com
• LinkedIn: linkedin.com/in/tino-penchev-quant
• GitHub: github.com/tinopenchev