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Vigilance estimation using truncated l1 distance kernel-based sparse representation regression with physiological

Xuan Zhang1, Dixin Wang1, Hongtong Wu1

  • 1Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.

Computer Methods and Programs in Biomedicine
|September 21, 2023
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Summary
This summary is machine-generated.

This study introduces a new vigilance estimation framework using the truncated l1 (TL1) kernel with sparse representation (SR). The TL1 kernel shows superior performance and stability over RBF for physiological signal analysis.

Keywords:
ElectroencephalogramElectrooculogramIndefinite kernelSparse representationVigilance estimation

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Biomedical Engineering

Background:

  • Operator vigilance decline causes accidents, necessitating automatic monitoring.
  • Physiological signals and machine learning offer objective vigilance estimation methods.

Purpose of the Study:

  • To evaluate the truncated l1 (TL1) kernel's adaptability and performance improvement for sparse representation (SR)-based vigilance estimation.
  • To propose a novel recognition framework integrating TL1 kernel with SR theory for physiological signal analysis.

Main Methods:

  • Utilized sparse representation (SR) and the truncated l1 (TL1) kernel for physiological signal processing.
  • Mapped physiological features to reproducing kernel Krein space via TL1 kernel's infinite-dimensional projection.
  • Employed eigenspectrum approaches for kernel matrix conversion and sparse representation regression for final prediction.

Main Results:

  • Validated the framework on the SEED-VIG dataset, using electroencephalogram and electrooculogram signals.
  • Demonstrated TL1 kernel's superiority over the radial basis function (RBF) kernel in performance and stability.
  • Achieved excellent vigilance estimation capabilities with the proposed framework.

Conclusions:

  • The TL1 kernel effectively distinguishes physiological signals for vigilance estimation.
  • The proposed framework offers a robust solution for objective operator vigilance monitoring.
  • This research encourages further development of kernel methods in physiological signal recognition.