Passive BCI Based on Sustained Attention Detection: An fNIRS Study

  • Zhen Zhang
  • Xuejun Jiao
  • Jin Jiang
  • Jinjin Pan
  • Yong Cao
  • Hanjun Yang
  • Fenggang Xu
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10023)

Abstract

Passive brain-computer interface (BCI) can monitor cognitive function through physiological signals in human-machine system. This paper established a passive BCI based on functional near-infrared spectroscopy (fNIRS) to detect the sustained attentional load. Three levels of attentional load were adjusted by modifying the number of stimulate in feature-absence Continuous Performance Test (CPT) tasks. 15 healthy subjects were recruited in total, and 10 channels were measured in prefrontal cortex (PFC). Performance and NASA-TLX scales were also recorded as reference. The mean value of oxyhemoglobin and deoxyhemoglobin, signal slope, power spectrum and approximate entropy in 0–10 s were extracted from raw fNIRS signal for support vector machine (SVM) classification. The best performance features were selected by SVM-RFE algorithm. In conclusion over 80% average accuracy was achived between easy and hard attentional load, which demonstrated fNIRS can be a proposed method to detect sustained attention load for a passive BCI.

Keywords

False Alarm Rate Motor Imagery Sustained Attention Simple Task Mental Workload 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

This research was supported in part by National Natural Science Foundation of China (grant no. 81671861), Advance Research Project of China Aerospace Medical Engineering (grant no. YJGF151204) and independent subject of National Key Laboratory of Human Factors Engineering, China Astronaut Research and training center, Beijing, China (grant no. SYFD150051805).

References

  1. 1.
    Zander, T.O., Kothe, C.: Towards passive brain-computer interfaces: applying brain-computer interface technology to human-machine systems in general. J. Neural Eng. 8(2), 025005 (2011)CrossRefGoogle Scholar
  2. 2.
    Young, L.R.: On adaptive manual control. Ergonomics 12(4), 635–674 (1969)CrossRefGoogle Scholar
  3. 3.
    Billings, C.E.: Aviation Automation: The Search for a Human-Centered Approach (1997)Google Scholar
  4. 4.
    Gaume, A., Abbasi, M.A., Dreyfus, G., et al.: Towards cognitive BCI: neural correlates of sustained attention in a continuous performance task. In: Proceedings of International IEEE EMBS Neural Engineering (2015)Google Scholar
  5. 5.
    Stroobant, N., Vingerhoets, G.: Transcranial doppler ultrasonography monitoring of cerebral hemodynamics during performance of cognitive tasks: a review. Neuropsychol. Rev. 10(4), 213–231 (2000)CrossRefGoogle Scholar
  6. 6.
    Warm, J.S., Matthews, G., Parasuraman, R.: Cerebral hemodynamics and vigilance performance. Mil. Psychol. 21(1), 75–100 (2009)CrossRefGoogle Scholar
  7. 7.
    Helton, W.S., Warm, J.S., Tripp, L.D., et al.: Cerebral lateralization of vigilance: a function of task difficulty. Neuropsychologia 48(6), 1683–1688 (2010)CrossRefGoogle Scholar
  8. 8.
    Cornblatt, B., Risch, N.J., Faris, G., Friedman, D., Erlenmeyer-Kimling, L.: The continuous performance test, identical pairs version (CPT-IP): I. New findings about sustained attention in normal families. Psychiatry Res. 29, 65–68 (1988)CrossRefGoogle Scholar
  9. 9.
    Adler, C.M., Sax, K.W., Holland, S.K., Schmithorst, V., Rosenberg, L., Strakowski, S.M.: Changes in neuronal activation with increasing attention demanding healthy volunteers: an fMRI study. Synapse 42, 266–272 (2001)CrossRefGoogle Scholar
  10. 10.
    Shalev, L., Ben-Simon, A., Mevorach, C., et al.: Conjunctive continuous performance task (CCPT) a pure measure of sustained attention. Neuropsychologia 49(9), 2584–2591 (2011)CrossRefGoogle Scholar
  11. 11.
    Catanzaro, J.M., Scerbo, M.W.: Searching for the presence and absence of features in vigilance: the decrement persists. Hum. Factors Ergon. Soc. Annu. Meet. Proc. 43(23), 1285–1288 (1999)CrossRefGoogle Scholar
  12. 12.
    Khan, M.J., Hong, K.S.: Passive BCI based on drowsiness detection: an fNIRS study. Biomed. Opt. Express 6(10), 4063–4078 (2015)CrossRefGoogle Scholar
  13. 13.
    Jiao, X., Bai, J., Chen, S., et al.: Monitoring mental fatigue in analog space environment using optical brain imaging. Engineering 05(5), 53–57 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Zhen Zhang
    • 1
  • Xuejun Jiao
    • 1
  • Jin Jiang
    • 1
  • Jinjin Pan
    • 1
  • Yong Cao
    • 1
  • Hanjun Yang
    • 1
  • Fenggang Xu
    • 1
  1. 1.National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training CenterBeijingChina

Personalised recommendations