Domain Adaptation Techniques for EEG-Based Emotion Recognition: A Comparative Study on Two Public Datasets

• Published in: IEEE transactions on cognitive and developmental systems Vol. 11; no. 1; pp. 85 - 94
• Main Authors: Lan, Zirui, Sourina, Olga, Wang, Lipo, Scherer, Reinhold, Muller-Putz, Gernot R.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.03.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Adaptation; Affective brain–computer interface (aBCI); Brain-computer interfaces; Classification; Classifiers; Comparative studies; cross dataset; Datasets; domain adaptation; Electroencephalography; electroencephalography (EEG); Emotion recognition; Feature extraction; Indexing; Motion pictures; Protocol (computers); State-of-the-art reviews; Task analysis; Training; transfer learning

Affective brain-computer interface (aBCI) introduces personal affective factors to human-computer interaction. The state-of-the-art aBCI tailors its classifier to each individual user to achieve accurate emotion classification. A subject-independent classifier that is trained on pooled data from mul...