title: Using robust principal component analysis to alleviate day-to-day variability in EEG based emotion classification.
abstract: An emerging challenge for emotion classification using electroencephalography (EEG) is how to effectively alleviate day-to-day variability in raw data. This study employed the robust principal component analysis (RPCA) to address the problem with a posed hypothesis that background or emotion-irrelevant EEG perturbations lead to certain variability across days and somehow submerge emotion-related EEG dynamics. The empirical results of this study evidently validated our hypothesis and demonstrated the RPCA's feasibility through the analysis of a five-day dataset of 12 subjects. The RPCA allowed tackling the sparse emotion-relevant EEG dynamics from the accompanied background perturbations across days. Sequentially, leveraging the RPCA-purified EEG trials from more days appeared to improve the emotion-classification performance steadily, which was not found in the case using the raw EEG features. Therefore, incorporating the RPCA with existing emotion-aware machine-learning frameworks on a longitudinal dataset of each individual may shed light on the development of a robust affective brain-computer interface (ABCI) that can alleviate ecological inter-day variability.
authors.lastname: Ping-Keng Jao
authors.lastname: Yuan-Pin Lin
authors.lastname: Yi-Hsuan Yang
authors.lastname: Tzyy-Ping Jung
mesh_terms: D000465:Algorithms; D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D004644:Emotions; D006801:Humans; D025341:Principal Component Analysis