@inproceedings{628520bcd877490791fcea5e9906c37a,
title = "Multitask Autoencoder-Based Two-Phase Framework Using Multilevel Feature Fusion for EEG Emotion Recognition",
abstract = "Emotion recognition has emerged as a active research area, gaining relevance from advancements in deep learning. This study focuses on using electroencephalogram (EEG) data for emotion recognition and addresses the challenge of subject-dependent variability in EEG-based emotion recognition by proposing a novel architecture that employs multilevel feature fusion and a multitask autoencoder-based two-phase framework. The first phase generates classspecific data, while the second phase uses these for model training. The proposed model was validated using the SEED dataset and demonstrated state-of-the art perforamnce with an accuracy of 99.4 \% in a subject-independent setting.",
keywords = "component, formatting, insert, style, styling",
author = "Jin, \{Chang Gyun\} and Shin, \{Chan Woo\} and Hanul Kim and Kim, \{Seong Eun\}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 International Conference on Electronics, Information, and Communication, ICEIC 2024 ; Conference date: 28-01-2024 Through 31-01-2024",
year = "2024",
doi = "10.1109/ICEIC61013.2024.10457197",
language = "English",
series = "2024 International Conference on Electronics, Information, and Communication, ICEIC 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2024 International Conference on Electronics, Information, and Communication, ICEIC 2024",
}