A review on semi-supervised learning for EEG-based emotion recognition

Abstract

Semisupervised learning holds significant academic and practical importance in the realm of EEG-based emotion recognition. Currently, a multitude of research endeavors are dedicated to addressing the challenge of emotion recognition, especially in light of the growing popularity of deep learning. Within this context, semisupervised learning has emerged as an innovative and promising methodological trend. This article seeks to contribute a comprehensive review that captures the research landscape, distills essential technologies, and projects the trajectory of semisupervised learning for EEG-based emotion recognition. The review encompasses various critical aspects. Firstly, it introduces EEG-based emotion recognition and underscores the pivotal role of semisupervised learning in enhancing its performance. Secondly, the semisupervised learning framework is analyzed, drawing comparisons with conventional methodologies to highlight its unique advantages. Thirdly, the article delves into the intricacies of the emotion recognition challenge, shedding light on how semisupervised learning can effectively address these complexities. Furthermore, the review synthesizes, categorizes, and provides exemplifications of representative semisupervised learning approaches that are specifically tailored for EEG-based emotion recognition. Finally, the methodological strengths of semisupervised learning are elucidated, accompanied by a discussion of the encountered challenges. The article concludes by projecting the future developmental trajectory of semisupervised learning within the domain of EEG-based emotion recognition, underlining its potential for inspiring innovation and advancements in this field.