International Conference on Neural Information Processing

Neural Information Processing pp 61-68 | Cite as

Transfer Components Between Subjects for EEG-based Driving Fatigue Detection

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9492)

Abstract

In this paper, we first build up an electroencephalogram (EEG)-based driving fatigue detection system, and then propose a subject transfer framework for this system via component analysis. We apply a subspace projecting approach called transfer component analysis (TCA) for subject transfer. The main idea is to learn a set of transfer components underlying source domain (source subjects) and target domain (target subjects). When projected to this subspace, the difference of feature distributions of both domains can be reduced. Meanwhile, the discriminative information can be preserved. From the experiments, we show that the TCA-based algorithm can achieve a significant improvement on performance with the best mean accuracy of 77.56 %, in comparison of the baseline accuracy of 66.56 %. The improvement shows the feasibility and efficiency of our approach for subject transfer driving fatigue detection from EEG.

Keywords

EEG Driving fatigue detection Transfer learning Domain adaptation 

References

  1. 1.
    Nobe, S., Wang, F.-Y., et al.: An overview of recent developments in automated lateral and longitudinal vehicle controls. In: 2001 IEEE International Conference on Systems, Man, and Cybernetics, vol. 5, pp. 3447–3452. IEEE (2001)Google Scholar
  2. 2.
    Cajochen, C., Khalsa, S.B.S., Wyatt, J.K., Czeisler, C.A., Dijk, D.-J.: EEG and ocular correlates of circadian melatonin phase and human performance decrements during sleep loss. Am. J. Physiol. Regul. Integr. Comp. Physiol. 277(3), R640–R649 (1999)Google Scholar
  3. 3.
    Buch, E., Weber, C., Cohen, L.G., Braun, C., Dimyan, M.A., Ard, T., Mellinger, J., Caria, A., Soekadar, S., Fourkas, A., et al.: Think to move: a neuromagnetic brain-computer interface (bci) system for chronic stroke. Stroke 39(3), 910–917 (2008)CrossRefGoogle Scholar
  4. 4.
    Krauledat, M., Tangermann, M., Blankertz, B., Müller, K.-R.: Towards zero training for brain-computer interfacing. PLOS One 3(8), 2967–2976 (2008)CrossRefGoogle Scholar
  5. 5.
    Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)CrossRefGoogle Scholar
  6. 6.
    Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Netw. 22(2), 199–210 (2011)CrossRefGoogle Scholar
  7. 7.
    Ben-David, S., Blitzer, J., Crammer, K., Pereira, F., et al.: Analysis of representations for domain adaptation. Adv. Neural Inf. Process. Syst. 19, 137 (2007)Google Scholar
  8. 8.
    Duan, R.-N., Zhu, J.-Y., Lu, B.-L.: Differential entropy feature for EEG-based emotion classification. In: IEEE EMBS Conference on Neural Engineering, pp. 81–84. IEEE (2013)Google Scholar
  9. 9.
    Shi, L.-C., Bao-Liang, L.: Eeg-based vigilance estimation using extreme learning machines. Neurocomputing 102, 135–143 (2013)CrossRefGoogle Scholar
  10. 10.
    Kuzniecky, R.: Symptomatic occipital lobe epilepsy. Cortex 3(12), 13 (1998)Google Scholar
  11. 11.
    Cheng, S.-Y., Hsu, H.-T.: Mental Fatigue Measurement Using EEG. INTECH Open Access Publisher, Rijeka (2011)CrossRefGoogle Scholar
  12. 12.
    Ngoc, H.T., Nguyen, T.H., Ngo, C.: Average partial power spectrum density approach to feature extraction for EEG-based motor imagery classification. Am. J. Biomed. Eng. 3(6), 208–219 (2013)Google Scholar
  13. 13.
    Ji, Q., Yang, X.: Real-time eye, gaze, and face pose tracking for monitoring driver vigilance. Real Time Imag. 8(5), 357–377 (2002)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Gao, X.-Y., Zhang, Y.-F., Zheng, W.-L., Lu, B.-L.: Evaluating driving fatigue detection algorithms using eye tracking glasses. In: IEEE EMBS Conference on Neural Engineering, pp. 767–770. IEEE (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Center for Brain-like Computing and Machine Intelligence, Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive EngineeringShanghai Jiao Tong UniversityShanghaiChina

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