May 15, 2024, 4:43 a.m. | Andrea Apicella, Pasquale Arpaia, Giovanni D'Errico, Davide Marocco, Giovanna Mastrati, Nicola Moccaldi, Roberto Prevete

cs.LG updates on

arXiv:2212.08744v2 Announce Type: replace
Abstract: 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 …

abstract architectures arxiv assessment classification context cs.lg dataset eeg emotion improving issue machine machine learning replace review shift strategies type

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