Deep Learning Method for Selecting Effective Models and Feature Groups in Emotion Recognition Using an Asian Multimodal Database

• Published in: Electronics (Basel) Vol. 9; no. 12; p. 1988
• Main Authors: Maeng, Jun-Ho, Kang, Dong-Hyun, Kim, Deok-Hwan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2020
• Subjects: Accuracy; Algorithms; Annotations; Applications programs; Arousal; Brain; Brain research; Classification; Datasets; Deep learning; Electroencephalography; Emotion recognition; Emotions; Experiments; Feature extraction; Feature recognition; Feature selection; Genetic algorithms; Machine learning; Methods; Neural networks; Physiology; Principal components analysis; Root-mean-square errors; Support vector machines

Emotional awareness is vital for advanced interactions between humans and computer systems. This paper introduces a new multimodal dataset called MERTI-Apps based on Asian physiological signals and proposes a genetic algorithm (GA)—long short-term memory (LSTM) deep learning model to derive the acti...