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http://dx.doi.org/10.9723/jksiis.2021.26.1.001

Emotion Classification based on EEG signals with LSTM deep learning method  

Kim, Youmin (엠로)
Choi, Ahyoung (가천대학교 소프트웨어학과)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.26, no.1, 2021 , pp. 1-10 More about this Journal
Abstract
This study proposed a Long-Short Term Memory network to consider changes in emotion over time, and applied an attention mechanism to give weights to the emotion states that appear at specific moments. We used 32 channel EEG data from DEAP database. A 2-level classification (Low and High) experiment and a 3-level classification experiment (Low, Middle, and High) were performed on Valence and Arousal emotion model. As a result, accuracy of the 2-level classification experiment was 90.1% for Valence and 88.1% for Arousal. The accuracy of 3-level classification was 83.5% for Valence and 82.5% for Arousal.
Keywords
EEG; Emotion classification; Long-Short Term Memory Network; Attention mechanism;
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