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An EEG monitoring method based on compressed sensing for fatigue driving.

Zengnian Xin
Published in: Computer methods in biomechanics and biomedical engineering (2024)
In order to reduce the amount of EEG collected by the fatigue driving monitoring system, this paper proposes a new EEG monitoring method based on compressed sensing. According to the driver's driving duration, the new proposed method adopts different sampling rate to compressive sample the EEG of the driver in different driving stages, and then realizes the accurate reconstruction of the compressed EEG data in the computer of vehicle. The experiment verifies effectiveness of the proposed method by collecting EEG signals from multiple subjects during simulated driving. The experimental results show that, compared with traditional sampling, the new method reduces the amount of sampled data by 3 orders of magnitude. And when the compression ratio is not higher than 70%, the reconstruction error is less than 0.16.
Keyphrases
  • functional connectivity
  • resting state
  • working memory
  • randomized controlled trial
  • systematic review
  • electronic health record
  • big data
  • depressive symptoms
  • deep learning
  • machine learning
  • mass spectrometry