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Deep Leaning-Based Approach for Mental Workload Discrimination from Multi-channel fNIRS

  • Thi Kieu Khanh Ho
  • Jeonghwan Gwak
  • Chang Min Park
  • Ashish Khare
  • Jong-In Song
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 524)

Abstract

As a non-invasive optical neuroimaging technique, functional near infrared spectroscopy (fNIRS) is currently used to assess brain dynamics during the performance of complex works and everyday tasks. However, the deep learning approaches to distinguish stress levels based on the changes of hemoglobin concentrations have not yet been extensively investigated. In this paper, we evaluated the efficiencies of advanced methods differentiating the rest and task periods during stroop task experiments. First, we explored that the apparent changes of oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) concentrations associated with two mental stages did exist across each participant. Then, a novel discrimination framework was studied. Deep learning approaches, including convolutional neural network (CNN), deep belief networks (DBN), have enabled better classification accuracies of 84.26 ± 9.10% and 65.43 ± 1.59% as our preliminary study.

Keywords

Stroop task experiments Functional near infrared spectroscopy Convolutional neural networks Deep belief networks 

Notes

Acknowledgements

This work was supported by the Basic Science Research Program through the NRF funded by the Ministry of Education (NRF-2017R1D1A1B03036423) and the Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2016M3C7A1905477, NRF-2014M3C7A1046050). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

References

  1. 1.
    Hoshi, Y., & Tamura, M. (1993). Detection of dynamic changes in cerebral oxygenation coupled to neuronal function during mental work in man. Neuroscience Letters, 150, 5–8.CrossRefGoogle Scholar
  2. 2.
    Nguyen, H. T., Van Nguyen, H., Truong, K. Q. D., & Van Vo, T. (2013). Analysis of oxy-Hb signals to determine relationship between jaw imbalance and arm strength using fNIRS. American Journal of Biomedical Engineering, 3, 107–118.Google Scholar
  3. 3.
    Ferrari, M., & Quaresima, V. (2012). A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application. Neuroimage, 63, 921–935.CrossRefGoogle Scholar
  4. 4.
    Hai, N. T., Cuong, N. Q., Khoa, T. Q. D., & Toi, V. V. (2013). Temporal hemodynamic classification of two hands tapping using functional near infrared spectroscopy. Frontiers in Human Neuroscience, 7, Article 516, 1–12.Google Scholar
  5. 5.
    Molteni, E., Baselli, G., Bianchi, A. M., Caffini, M., Contini, D., Spinelli, L. et al. (2009). Frontal brain activation during a working memory task: A time-domain fNIRS study. Photonic Therapeutics and Diagnostics V, 71613 N.  https://doi.org/10.1117/12.808972.
  6. 6.
    Sassaroli, A., Zheng, F., Coutts, M., Hirshfield, L. H., Girouard, A., Solovey, E. T., et al. (2009). Application of near-infrared spectroscopy for discrimination of mental workloads. In Proceedings of SPIE 7174, Optical Tomography and Spectroscopy of Tissue VIII (pp. 71741H).Google Scholar
  7. 7.
    Ramnani, N., & Owen, A. M. (2004). Anterior prefrontal cortex: Insights into function from anatomy and neuroimaging. Nature Reviews Neuroscience, 5, 184–194.CrossRefGoogle Scholar
  8. 8.
    Suthaharan, S. (2016). Support vector machine. In Machine learning models and algorithms for big data classification (pp. 207–235). Springer.Google Scholar
  9. 9.
    Chu, F., & Zaniolo, C. (2004). Fast and light boosting for adaptive mining of data streams. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 282–292). Springer.Google Scholar
  10. 10.
    Hinton, G., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18, 1527–1554.MathSciNetCrossRefGoogle Scholar
  11. 11.
    LeCun, Y., Kavukcuoglu, K., & Farabet, C. (2010). Convolutional networks and applications in vision. In Proceedings of the IEEE International Symposium on Circuits and Systems (pp 253–226).Google Scholar
  12. 12.
    Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). ImageNet classification with deep convolutional neural networks. In Proceedings of NIPS (pp. 1097–1105).Google Scholar
  13. 13.
    Tsinalis, O., Matthews, P. M., Guo, Y., & Zafeiriou, S. (2016). Automatic sleep stage scoring with single-channel EEG using convolutional neural networks. arXiv:1610.01683.
  14. 14.
    Schroeter, M. L., et al. (2002). Near-infrared spectroscopy can detect brain activity during a color–word matching Stroop task in an event-related design. Human Brain Mapping, 17(1), 61–71.CrossRefGoogle Scholar
  15. 15.
    Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. CoRR. arXiv:1409.1556.
  16. 16.
    Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2014). Overfeat: Integrated recognition, localization and detection using convolutional networks. In ICLR.Google Scholar
  17. 17.
    Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 580–587).Google Scholar
  18. 18.
    Baker, W. B., Parthasarathy, A. B., Busch, D. R., Mesquita, R. C., Greenberg, J. H., & Yodh, A. G. (2014). Modified Beer-Lambert law for blood flow. Biomedical Optics Express, 5, 4053–4075.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Thi Kieu Khanh Ho
    • 1
    • 7
  • Jeonghwan Gwak
    • 1
    • 2
  • Chang Min Park
    • 2
    • 3
    • 4
    • 5
  • Ashish Khare
    • 6
  • Jong-In Song
    • 7
  1. 1.Biomedical Research InstituteSeoul National University HospitalSeoulKorea
  2. 2.Department of RadiologySeoul National University HospitalSeoulKorea
  3. 3.Department of RadiologySeoul National University College of MedicineSeoulKorea
  4. 4.Institute of Radiation MedicineSeoul National University Medical Research CenterSeoulKorea
  5. 5.Seoul National University Cancer Research InstituteSeoulKorea
  6. 6.Department of Electronics and CommunicationUniversity of AllahabadAllahabadIndia
  7. 7.School of Electrical Engineering and Computer ScienceGwangju Institute of Science and TechnologyGwangjuKorea

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