Learning-Based Instantaneous Drowsiness Detection Using Wired and Wireless Electroencephalography

Hyun Soo Choi, Seonwoo Min, Siwon Kim, Ho Bae, Jee Eun Yoon, Inha Hwang, Dana Oh, Chang Ho Yun, Sungroh Yoon

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Instantaneous drowsiness (i.e., lapse or micro-sleep) during various activities such as driving or construction causes enormous socioeconomic losses. Thus, a virtuous cycle system that monitors a subject's drowsiness can improve work efficiency and safety. We propose a novel framework to detect instantaneous drowsiness with only a two-second length of electroencephalography (EEG). To achieve reliable performance, we use multitaper power spectral density for feature extraction along with extreme gradient boosting as a machine learning classifier. In addition, we introduce a novel phenotype labeling of instantaneous drowsiness by combining both task dependent and independent measures of alertness (psychomotor vigilance task and electrooculography technique, respectively). The results show that our techniques outperform others used in previous studies. We also identified which spectral components (θ, α, and γ) and channels (Fp1, Fp2, T3, T4, O1, O2, and electrocardiogram) play important roles in our drowsiness detection framework. To verify the applicability for a mobile environment, we implemented our framework on a wireless EEG, as well as on a wired EEG. We hereby present our successful results.

Original languageEnglish
Article number8861308
Pages (from-to)146390-146402
Number of pages13
JournalIEEE Access
StatePublished - 2019

Bibliographical note

Publisher Copyright:
© 2019 IEEE.


  • Drowsiness
  • electroencephalography
  • lapse
  • wireless electroencephalography


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