TY  - JOUR
A1  - Moontaha, Sidratul
A1  - Schumann, Franziska Elisabeth Friederike
A1  - Arnrich, Bert
T1  - Online learning for wearable EEG-Based emotion classification
T2  - Sensors
N2  - Giving emotional intelligence to machines can facilitate the early detection and prediction of mental diseases and symptoms. Electroencephalography (EEG)-based emotion recognition is widely applied because it measures electrical correlates directly from the brain rather than indirect measurement of other physiological responses initiated by the brain. Therefore, we used non-invasive and portable EEG sensors to develop a real-time emotion classification pipeline. The pipeline trains different binary classifiers for Valence and Arousal dimensions from an incoming EEG data stream achieving a 23.9% (Arousal) and 25.8% (Valence) higher F1-Score on the state-of-art AMIGOS dataset than previous work. Afterward, the pipeline was applied to the curated dataset from 15 participants using two consumer-grade EEG devices while watching 16 short emotional videos in a controlled environment. Mean F1-Scores of 87% (Arousal) and 82% (Valence) were achieved for an immediate label setting. Additionally, the pipeline proved to be fast enough to achieve predictions in real-time in a live scenario with delayed labels while continuously being updated. The significant discrepancy from the readily available labels on the classification scores leads to future work to include more data. Thereafter, the pipeline is ready to be used for real-time applications of emotion classification.
KW  - online learning
KW  - real-time
KW  - emotion classification
KW  - AMIGOS dataset
KW  - wearable EEG (muse and neurosity crown)
KW  - psychopy experiments
Y1  - 2023
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/64229
SN  - 1424-8220
VL  - 23
IS  - 5
PB  - MDPI
CY  - Basel
ER  -