Temporal EEG imaging for drowsy driving prediction

Eric Juwei Cheng*, Ku Young Young, Chin Teng Lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


As a major cause of vehicle accidents, the prevention of drowsy driving has received increasing public attention. Precisely identifying the drowsy state of drivers is difficult since it is an ambiguous event that does not occur at a single point in time. In this paper, we use an electroencephalography (EEG) image-based method to estimate the drowsiness state of drivers. The driver's EEG measurement is transformed into an RGB image that contains the spatial knowledge of the EEG. Moreover, for considering the temporal behavior of the data, we generate these images using the EEG data over a sequence of time points. The generated EEG images are passed into a convolutional neural network (CNN) to perform the prediction task. In the experiment, the proposed method is compared with an EEG image generated from a single data time point, and the results indicate that the approach of combining EEG images in multiple time points is able to improve the performance for drowsiness prediction.

Original languageEnglish
Article number5078
JournalApplied Sciences (Switzerland)
Issue number23
StatePublished - 1 Dec 2019


  • Convolutional neural network
  • Deep learning
  • Driving fatigue
  • Electroencephalography
  • Feature extraction


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