Boosting Transfer Learning Improves Performance of Driving Drowsiness Classification Using EEG

Jiancong He, Guoxu Zhou, Hongtao Wang, Evangelos Sigalas, Nitish V Thakor, Anastasios Bezerianos, Junhua Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Drowsy driving poses considerable risk not only to drivers themselves but also to other people on the road. It has been demonstrated that information contained in electroencephalography (EEG) signal can be used to identify driving drowsiness. To date, most of work focused on the detection of drowsiness within a session. This hampers the generalization of the trained model to a following session conducted after a few days. As we know, EEG is non-stationary and changes dramatically across sessions, which leads to a great challenge how to establish a model that has a good performance across sessions. In this study, we combined boosting strategy and transfer learning method to establish a model for identifying driving drowsiness states from alertness states based on the features of power spectral density (PSD). The model trained using the data collected a few days ago (session1) was tuned using very small portion of the data collected in the current session can achieve a good performance as tested in the current session (session2). The results demonstrated that the proposed boosting transfer learning method significantly outperformed the support vector machine (SVM) and AdaBoost methods. The proposed method could promote practical use of drowsiness detection system in a real vehicle due to its good cross-session performance.

Original languageEnglish (US)
Title of host publication2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781538668597
DOIs
StatePublished - Jul 31 2018
Externally publishedYes
Event2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018 - Singapore, Singapore
Duration: Jun 12 2018Jun 14 2018

Other

Other2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018
CountrySingapore
CitySingapore
Period6/12/186/14/18

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Radiology Nuclear Medicine and imaging
  • Behavioral Neuroscience
  • Cognitive Neuroscience
  • Neurology

Fingerprint Dive into the research topics of 'Boosting Transfer Learning Improves Performance of Driving Drowsiness Classification Using EEG'. Together they form a unique fingerprint.

  • Cite this

    He, J., Zhou, G., Wang, H., Sigalas, E., Thakor, N. V., Bezerianos, A., & Li, J. (2018). Boosting Transfer Learning Improves Performance of Driving Drowsiness Classification Using EEG. In 2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018 [8423951] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PRNI.2018.8423951