Multi-Computations

A comprehensive framework for multimodal data analysis and processing, focusing on EEG, gaze tracking, and working memory task computations.

Overview

This project provides a suite of tools and utilities for processing and analyzing multiple types of experimental data, specifically designed for cognitive science and neuroscience research. It includes modules for:

• EEG data processing and analysis
• Gaze tracking data processing
• Working memory task computations
• Synthetic data generation for testing
• Comprehensive reporting tools

Project Structure

.
├── docs/               # Documentation files
├── src/               # Source code
│   ├── EEG-Py/       # EEG processing modules
│   ├── Gaze-Python/  # Gaze tracking analysis
│   ├── WM-Tasks/     # Working memory task implementations
│   ├── python/       # Core Python utilities and reporting
│   └── utility/      # Common utility functions
├── testing/          # Test files and test data
└── requirements.txt  # Project dependencies

Installation

1. Clone the repository:

git clone https://github.com/yourusername/Multi-Computations.git
cd Multi-Computations

2. Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

Requirements

• Python 3.8+
• Dependencies listed in requirements.txt:
  • pandas >= 1.3.0
  • numpy >= 1.20.0
  • matplotlib >= 3.4.0
  • scipy >= 1.7.0
  • mne >= 0.23.0
  • scikit-learn >= 0.24.0
  • And more...

Usage

Each component has its own specific usage instructions. Please refer to the README files in each subdirectory for detailed information:

• EEG Processing
• Gaze Tracking
• Working Memory Tasks
• Reporting Tools

Testing

The project includes comprehensive test suites and synthetic data generation for testing purposes:

python test_synthetic.py  # Run synthetic data tests
python -m pytest testing/ # Run all tests

License

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

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Contact

For questions and support, please open an issue in the GitHub repository.