Article
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Preserved in Portico This version is not peer-reviewed
Online Learning for Wearable EEG-based Emotion Classification
Version 1
: Received: 6 January 2023 / Approved: 9 January 2023 / Online: 9 January 2023 (09:09:08 CET)
A peer-reviewed article of this Preprint also exists.
Moontaha, S.; Schumann, F.E.F.; Arnrich, B. Online Learning for Wearable EEG-Based Emotion Classification. Sensors 2023, 23, 2387. Moontaha, S.; Schumann, F.E.F.; Arnrich, B. Online Learning for Wearable EEG-Based Emotion Classification. Sensors 2023, 23, 2387.
Abstract
Emotions are indicators of affective states and play a significant role in human daily life, behavior, and interactions. Giving emotional intelligence to the machines could, for instance, facilitate early detection and prediction of (mental) diseases and symptoms. Electroencephalography (EEG) -based emotion recognition is being widely applied because it measures electrical correlates directly from the brain rather than the indirect measurement of other physiological responses initiated by the brain. The recent development of non-invasive and portable EEG sensors makes it possible to use them in real-time applications. Therefore, this paper presents a real-time emotion classification pipeline, which trains different binary classifiers for the dimensions of Valence and Arousal from an incoming EEG data stream. After achieving a 23.9% (Arousal) and 25.8% (Valence) higher f1-score on the state-of-art AMIGOS dataset, this pipeline was applied to the dataset achieved by an emotion elicitation experimental framework developed within the scope of this thesis. Following two different protocols, 15 participants were recorded using two different consumer-grade EEG devices while watching 16 short emotional videos in a controlled environment. For an immediate label setting, the mean f1-score of 87% and 82% were achieved for Arousal and Valence, respectively. In a live scenario, while continuously being updated on the incoming data stream with delayed labels, the pipeline proved to be fast enough to achieve predictions in real time. However, the significant discrepancy from the readily available labels on the classification scores leads to future work to include more data with frequent delayed labels in the live settings.
Keywords
Online Learning; Emotion Classification; AMIGOS dataset; Wearable-EEG (Muse and Neurosity Crown); Psychopy Experiments
Subject
Computer Science and Mathematics, Other
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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