Can Emotion Be Transferred?—A Review on Transfer Learning for EEG-Based Emotion Recognition
Wei Li, Wei Huan, Bowen Hou, Ye Tian, Zhen Zhang, Aiguo Song
Abstract
The issue of electroencephalogram (EEG)-based emotion recognition has great academic and practical significance. Currently, there are numerous research trying to address this issue in the literature. Particularly, transfer learning has gradually become a new methodological trend for the issue in company with the popularity of deep learning. Motivated by capturing the research panorama, summarizing the technological essence, and forecasting the advancement tendency of transfer learning for EEG-based emotion recognition, this article contributes a review work. This work mainly includes five aspects: 1) introducing the issue of EEG-based emotion recognition and expounding the importance of transfer learning for it; 2) analyzing the transfer learning framework and comparing it with the traditional ones; 3) elucidating the issue difficulties and explaining the suitability and capability of transfer learning for this issue; 4) summarizing, categorizing, and exemplifying the typical transfer learning methods for this issue; and 5) clarifying the methodological merits, discussing the challenging problems, and predicting the prospective development of transfer learning for the issue. We expect these contributions can inspire innovation and reformation of the transfer learning methodology for EEG-based emotion recognition as well as other relevant topics in the not-so-far future.