  A systematic review on machine-learning strategies for improving
generalizability (cross-subjects and cross-sessions) electroencephalography
(EEG) based in emotion classification was realized. In this context, the
non-stationarity of 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. 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
EEG-based emotion assessment. Among these papers, 75 were found eligible based
on their relevance to the problem. Studies lacking a specific cross-subject and
cross-session validation strategy and 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, together with a
brief discussion on 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.
