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

Fingerprint

Sleep Stages
Electroencephalography
Adaptive boosting
Power spectral density
Support vector machines
Transfer (Psychology)

ASJC Scopus subject areas

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

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

Boosting Transfer Learning Improves Performance of Driving Drowsiness Classification Using EEG. / He, Jiancong; Zhou, Guoxu; Wang, Hongtao; Sigalas, Evangelos; Thakor, Nitish V; Bezerianos, Anastasios; Li, Junhua.

2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8423951.

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

He, J, Zhou, G, Wang, H, Sigalas, E, Thakor, NV, 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., 2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018, Singapore, Singapore, 6/12/18. https://doi.org/10.1109/PRNI.2018.8423951
He J, Zhou G, Wang H, Sigalas E, Thakor NV, Bezerianos A et al. Boosting Transfer Learning Improves Performance of Driving Drowsiness Classification Using EEG. In 2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8423951 https://doi.org/10.1109/PRNI.2018.8423951
He, Jiancong ; Zhou, Guoxu ; Wang, Hongtao ; Sigalas, Evangelos ; Thakor, Nitish V ; Bezerianos, Anastasios ; Li, Junhua. / Boosting Transfer Learning Improves Performance of Driving Drowsiness Classification Using EEG. 2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018. Institute of Electrical and Electronics Engineers Inc., 2018.
@inproceedings{c71ba33144ae468796147b98bf95f478,
title = "Boosting Transfer Learning Improves Performance of Driving Drowsiness Classification Using EEG",
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.",
author = "Jiancong He and Guoxu Zhou and Hongtao Wang and Evangelos Sigalas and Thakor, {Nitish V} and Anastasios Bezerianos and Junhua Li",
year = "2018",
month = "7",
day = "31",
doi = "10.1109/PRNI.2018.8423951",
language = "English (US)",
isbn = "9781538668597",
booktitle = "2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Boosting Transfer Learning Improves Performance of Driving Drowsiness Classification Using EEG

AU - He, Jiancong

AU - Zhou, Guoxu

AU - Wang, Hongtao

AU - Sigalas, Evangelos

AU - Thakor, Nitish V

AU - Bezerianos, Anastasios

AU - Li, Junhua

PY - 2018/7/31

Y1 - 2018/7/31

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85051566217&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85051566217&partnerID=8YFLogxK

U2 - 10.1109/PRNI.2018.8423951

DO - 10.1109/PRNI.2018.8423951

M3 - Conference contribution

AN - SCOPUS:85051566217

SN - 9781538668597

BT - 2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -