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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
Young, L.R.: On adaptive manual control. Ergonomics 12(4), 635–674 (1969)
Billings, C.E.: Aviation Automation: The Search for a Human-Centered Approach (1997)
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)
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)
Warm, J.S., Matthews, G., Parasuraman, R.: Cerebral hemodynamics and vigilance performance. Mil. Psychol. 21(1), 75–100 (2009)
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)
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)
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)
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)
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)
Khan, M.J., Hong, K.S.: Passive BCI based on drowsiness detection: an fNIRS study. Biomed. Opt. Express 6(10), 4063–4078 (2015)
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)
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Zhang, Z. et al. (2016). Passive BCI Based on Sustained Attention Detection: An fNIRS Study. In: Liu, CL., Hussain, A., Luo, B., Tan, K., Zeng, Y., Zhang, Z. (eds) Advances in Brain Inspired Cognitive Systems. BICS 2016. Lecture Notes in Computer Science(), vol 10023. Springer, Cham. https://doi.org/10.1007/978-3-319-49685-6_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-49685-6_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-49684-9
Online ISBN: 978-3-319-49685-6
eBook Packages: Computer ScienceComputer Science (R0)