Machine Learning Strategies to Improve Cross-Subject and Cross-Session Generalization in EEG-Based Emotion Recognition: A Systematic Review

A systematic review on machine-learning strategies for improving generalization in electroencephalography-based emotion classification was realized. In particular, cross-subject and cross-session generalization was focused. In this context, the non-stationarity of electroencephalographic (EEG) signals is a critical issue and can lead to the Dataset Shift problem. Several architectures and methods have been proposed to address this issue, mainly based on transfer learning methods. In this review, 418 papers were retrieved from the Scopus, IEEE Xplore, and PubMed databases through a search query focusing on modern machine learning techniques for generalization in EEGbased emotion assessment. Among these papers, 75 were found eligible based on their relevance to the problem. Studies lacking a specific cross-subject or cross-session validation strategy, or making use of other biosignals as support were excluded. On the basis of the selected papers’ analysis, a taxonomy of the studies employing Machine Learning (ML) methods was proposed, to gether with a brief discussion of the different ML approaches involved. The studies with the best results in terms of average classification accuracy were identified, supporting that transfer learning methods seem to perform better than other approaches. A discussion is proposed on the impact of (i) the emotion theoretical models and (ii) psychological screening of the experimental sample on the classifier performances.