SPARK: A High-Efficiency Black-Box Domain Adaptation Framework for Source Privacy-Preserving Drowsiness Detection

• Published in: IEEE journal of biomedical and health informatics Vol. 28; no. 6; pp. 3478 - 3488
• Main Authors: Yuan, Liqiang, Li, Ruilin, Cui, Jian, Siyal, Mohammed Yakoob
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation; Adaptation models; Adult; Algorithms; Availability; Biomedical monitoring; Black-box domain adaptation (BBDA); Brain modeling; Closed box; Data models; Datasets; Deep Learning; Distillation; driver drowsiness detection; Driver fatigue; Drowsiness; EEG; Electroencephalography; Electroencephalography - methods; ensemble deep random vector functional link (edRVFL); Ensemble learning; Humans; Labelling; Labels; Monitoring; Privacy; Risk reduction; Signal Processing, Computer-Assisted; Sleep Stages - physiology; Sleepiness; Traffic accidents & safety; Traffic safety; Training; unsupervised domain adaptation (UDA)

Developing an effective and efficient electroencephalography (EEG)-based drowsiness monitoring system is crucial for enhancing road safety and reducing the risk of accidents. For general usage, cross-subject evaluation is indispensable. Despite progress in unsupervised domain adaptation (UDA) and so...