Multi-Output Regression for Integrated Prediction of Valence and Arousal in EEG-Based Emotion Recognition

This repository contains the code for the multi-output regression model used to predict the correlated dimensions of valence and arousal in EEG-based emotion recognition, aiming to improve prediction accuracy and efficiency. This project is based on the torcheeg framework.

Methods

• Single-output Regression: Predicts valence and arousal independently.
• Multi-output Regression: Predicts valence and arousal simultaneously, considering their interdependencies.
• Multi-output Regression with Chain Structure: Predicts valence first and uses it to predict arousal, reflecting the psychological sequence of emotional assessment.

Datasets and Models

Datasets	Models
DEAP	CCNN
GAMEEMO	LGGNet

Results

Notebooks

• DEAP_SD.ipynb : Subject-Dependent project
• GAMEEMO_SI.ipynb : Subject-Independent project

Citation

@inproceedings{choi2024multi,
  title={Multi-Output Regression for Integrated Prediction of Valence and Arousal in EEG-Based Emotion Recognition},
  author={Choi, HyoSeon and Woo, ChaeEun and Kong, JiYun and Kim, Byung Hyung},
  booktitle={2024 12th International Winter Conference on Brain-Computer Interface (BCI)},
  pages={1--6},
  year={2024},
  organization={IEEE}
}

LICENSE

This repository has a MIT license, as found in the LICENSE file.

Contact

For any questions or issues, please contact HyoSeon Choi at 13eye42@inha.edu.