Improving Cross-Day EEG-Based Emotion Classification Using Robust Principal Component Analysis

• Published in: Frontiers in computational neuroscience Vol. 11; p. 64
• Main Authors: Lin, Yuan-Pin, Jao, Ping-Keng, Yang, Yi-Hsuan
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 19.07.2017; Frontiers Media S.A
• Subjects: affective brain-computer interface; Brain research; Classification; Computer science; Computers; EEG; EEG oscillation; Electroencephalography; emotion classification; Emotions; inter-day EEG variability; International conferences; Learning algorithms; Listening; Music; Neuroscience; Oscillations; Physiology; Prediction models; robust principal component analysis; Signal processing; Workloads; Taiwan; robust principal component analysis; EEG oscillation; inter-day EEG variability; emotion classification; affective brain-computer interface
• ISSN: 1662-5188; 1662-5188
• DOI: 10.3389/fncom.2017.00064

Constructing a robust emotion-aware analytical framework using non-invasively recorded electroencephalogram (EEG) signals has gained intensive attentions nowadays. However, as deploying a laboratory-oriented proof-of-concept study toward real-world applications, researchers are now facing an ecological challenge that the EEG patterns recorded in real life substantially change across days (i.e., day-to-day variability), arguably making the pre-defined predictive model vulnerable to the given EEG signals of a separate day. The present work addressed how to mitigate the inter-day EEG variability of emotional responses with an attempt to facilitate cross-day emotion classification, which was less concerned in the literature. This study proposed a robust principal component analysis (RPCA)-based signal filtering strategy and validated its neurophysiological validity and machine-learning practicability on a binary emotion classification task (happiness vs. sadness) using a five-day EEG dataset of 12 subjects when participated in a music-listening task. The empirical results showed that the RPCA-decomposed sparse signals (RPCA-S) enabled filtering off the background EEG activity that contributed more to the inter-day variability, and predominately captured the EEG oscillations of emotional responses that behaved relatively consistent along days. Through applying a realistic add-day-in classification validation scheme, the RPCA-S progressively exploited more informative features (from 12.67 ± 5.99 to 20.83 ± 7.18) and improved the cross-day binary emotion-classification accuracy (from 58.31 ± 12.33% to 64.03 ± 8.40%) as trained the EEG signals from one to four recording days and tested against one unseen subsequent day. The original EEG features (prior to RPCA processing) neither achieved the cross-day classification (the accuracy was around chance level) nor replicated the encouraging improvement due to the inter-day EEG variability. This result demonstrated the effectiveness of the proposed method and may shed some light on developing a realistic emotion-classification analytical framework alleviating day-to-day variability.