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Real-time EEG-based emotion recognition using Riemannian quantification learning
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DOI：10.1016/j.bspc.2025.109223.png)
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Abstract
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• A novel Riemannian classifier is utilized for EEG-based emotion recognition. • The number of EEG channels and training samples was optimized across three datasets. • The effectiveness of the proposed approach is validated in an online scenario.
Keywords:
Real-time emotion recognition
Riemannian geometry
Electroencephalogram (EEG)
Channel selection
Minimal user training
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