Drowsiness monitoring with EEG-based MEMS biosensing technologies

Chih Wei Chang, Li-Wei Ko, Fu Chang Lin, Tung Ping Su, Tzyy Ping Jung, Chin Teng Lin, Jin-Chern Chiou*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

6 Scopus citations

Abstract

Electroencephalography (EEG) has been widely adopted to monitor changes in cognitive states, particularly stages of sleep, as EEG recordings contain a wealth of information reflecting changes in alertness and sleepiness. In this study, silicon dry electrodes based on Micro-Electro-Mechanical Systems (MEMS) were developed to bring high-quality EEG acquisition to operational workplaces. They have superior conductivity performance, large signal intensity, and are smaller in size than conventional (wet) electrodes. An EEG-based drowsiness estimation system consisting of a dry-electrode array, power spectrum estimation, principal component analysis (PCA)-based EEG signal analysis, and multivariate linear regression was developed to estimate drivers' drowsiness levels in a virtual-reality-based dynamic driving simulator. The proposed system can help elders who are often affected by periods of tiredness and fatigue.

Original languageEnglish
Pages (from-to)107-113
Number of pages7
JournalGeroPsych: The Journal of Gerontopsychology and Geriatric Psychiatry
Volume23
Issue number2
DOIs
StatePublished - 15 Dec 2010

Keywords

  • EEG
  • MEMS
  • PCA
  • aging
  • cognitive state
  • drowsiness
  • dry electrode
  • technology

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