Real-Time Positive Emotion Recognition Using the Positive Unlabeled Learning Method in a Brain Computer Interface System
DOI:
https://doi.org/10.24297/ijct.v25i.9810Keywords:
EEG, PU learning, emotion recognitionAbstract
Precise identification of emotional states is critical for affective computing applications—ranging from adaptive human–computer interfaces to clinical mental-health assessments. Traditional vision-based systems, however, lose effectiveness when facial expressivity is compromised (e.g., in Alzheimer’s or Bell’s palsy), driving interest in Electroencephalography (EEG)-based approaches. Yet, assembling large, reliably labeled EEG emotion datasets remains a major hurdle. To address this, we introduce a Brain–Computer Interface (BCI) framework that employs Positive–Unlabeled learning, training on a small, labeled subset alongside sufficient unlabeled data for preliminary evaluation. Coupled with a low-cost, portable EEG headset, our design minimizes equipment complexity without sacrificing performance. Validation shows an offline classification accuracy of 86.77% and a 86.20% success rate in real-time trials, confirming the method’s robustness and applicability.
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Copyright (c) 2025 Zizhu Li, Chengyuan Shen, Liangyu Zhao, Taiyo Maeda, Jianting Cao
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