Learning CNN features from DE features for EEG-based emotion recognition

• Published in: Pattern analysis and applications : PAA Vol. 23; no. 3; pp. 1323 - 1335
• Main Authors: Hwang, Sunhee, Hong, Kibeom, Son, Guiyoung, Byun, Hyeran
• Format: Journal Article
• Language: English
• Published: London Springer London 01.08.2020; Springer Nature B.V
• Subjects: Artificial neural networks; Computer Science; Computer vision; Datasets; Electrodes; Electroencephalography; Emotion recognition; Emotions; Feature extraction; Feature recognition; Frequencies; Machine learning; Natural language processing; Neural networks; Pattern Recognition; Representations; Signal classification; Signal processing; Theoretical Advances; Topology; Emotion recognition; EEG classification; Convolutional neural network

Recently, deep neural networks (DNNs) have shown the remarkable success of feature representations in computer vision, audio analysis, and natural language processing. Furthermore, DNNs have been used for electroencephalography (EEG) signal classification in recent studies on brain–computer interfac...