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Updated: Jun 16, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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

Liqiang Yuan, Ruilin Li, Jian Cui

IEEE Journal of Biomedical and Health Informatics

|March 14, 2024

Summary

A new privacy-preserving method for electroencephalography (EEG)-based drowsiness detection uses black-box domain adaptation. This approach enhances road safety by enabling accurate drowsiness monitoring without accessing sensitive source data.

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Area of Science:

Neuroscience
Computer Science
Road Safety

Background:

Electroencephalography (EEG)-based drowsiness monitoring is vital for road safety.
Existing domain adaptation methods for EEG analysis often require source data or models, raising privacy concerns.
Cross-subject evaluation is essential for generalizable EEG drowsiness detection.

Purpose of the Study:

To introduce a novel black-box domain adaptation (BBDA) framework for EEG-based drowsiness detection.
To address privacy risks associated with traditional unsupervised and source-free domain adaptation methods.
To develop an efficient and robust system for real-world drowsiness monitoring.

Main Methods:

Proposed the Self-distillation and Pseudo-labelling for Ensemble Deep Random Vector Functional Link (edRVFL)-based Black-box Knowledge Adaptation (SPARK) framework.
Utilized entropy-based selection for high-confidence sample pseudo-labeling.
Employed ensemble self-distillation for knowledge extraction and student model refinement.

Main Results:

SPARK demonstrated superior performance compared to strong baselines on two public driver drowsiness datasets.
The framework significantly reduced training time, enhancing computational efficiency.
The edRVFL features contributed to efficiency, particularly for small datasets.

Conclusions:

The SPARK framework offers an effective and privacy-preserving solution for EEG-based drowsiness detection.
SPARK shows potential for practical integration into real-world monitoring systems.
The study highlights the feasibility of BBDA for sensitive physiological data analysis.