OGSSL: A Semi-Supervised Classification Model Coupled with Optimal Graph Learning for EEG Emotion Recognition

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 30; p. 1
• Main Authors: Peng, Yong, Jin, Fengzhe, Kong, Wanzeng, Nie, Feiping, Lu, Bao-Liang, Cichocki, Andrzej
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Brain modeling; Central nervous system; Data models; EEG; Electroencephalogram (EEG); Electroencephalography; Emotion recognition; Emotions; feature selection; Frequencies; graph learning; Learning; semi-supervised learning; Semisupervised learning; Supervised learning; Task analysis

Electroencephalogram (EEG) signals are generated from central nervous system which are difficult to disguise, leading to its popularity in emotion recognition. Recently, semi-supervised learning exhibits promising emotion recognition performance by involving unlabeled EEG data into model training. H...