This project aims to advance the field of affective computing by improving the accuracy and efficiency of emotion recognition using electroencephalography (EEG) signals. By integrating adaptive resolution graph clustering with advanced signal processing techniques, we propose a novel approach to analyze complex EEG data, enabling more nuanced identification of emotional states.
Emotion recognition plays a pivotal role in affective computing, impacting various applications from healthcare to interactive technology. Traditional EEG-based emotion recognition systems face challenges due to the complexity and variability of brain signals. Our research addresses these challenges by employing adaptive resolution graph clustering, enhancing the granularity and computational efficiency of EEG signal analysis.
- To develop an adaptive resolution graph clustering framework for EEG data analysis.
- To improve the accuracy and computational efficiency of emotion recognition from EEG signals.
- To validate our approach using publicly available EEG datasets and compare its performance with existing methodologies.
- Python 3.8 or later
- MNE-Python
- NumPy, SciPy, and Matplotlib
- Jupyter Notebook (for running and viewing experiments)
Clone the repository to your local machine:
git clone https://github.com/your-username/your-research-project-name.git
Navigate to the project directory:
cd /some/where/CS-Group
Install the required Python packages:
pip install -r requirements.txt
This research utilizes the following EEG datasets:
- DEAP: A Database for Emotion Analysis using Physiological Signals
- SEED: A dataset for EEG-based emotion recognition
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Briefly describe the results of your research, including improvements in emotion recognition accuracy and computational efficiency. Include figures or tables if applicable.
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We welcome contributions from the community. If you're interested in contributing, please read our CONTRIBUTING.md for more information on how to get started.
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If you use this project or the adaptive resolution graph clustering method in your research, please cite our work:
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For more information on this research project, please contact Your Name.
### Notes:
- Replace placeholders (like `your-username`, `your-research-project-name`, `YourCitationHere`, etc.) with actual information about your project.
- Adjust the content under sections like **Usage**, **Results**, and **Acknowledgments** based on the specific details and outcomes of your research.
- Remember to add CONTRIBUTING.md and LICENSE files to your repository to provide detailed contributing guidelines and license information.