Abstract
The paper describes approach to instant of olfactory stimulus perception detection. Classification of olfactory stimuli in EEG is complex, but very important task. It allows to describe cognitive process and help in medical diagnosis process. Due to chemical - electrical nature of olfactory perception, there is need of solution which provide detection of beginning stimuli in EEG signal. Other way classification of olfactory stimuli would be more complex, due to not accurate in objects localization in learning set. Therefore the paper proposes utilization of Hilbert-Huang transformation in pre-processing. Proposed approach is evaluated and it have proven it’s usability.
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
Bushdid, C., Magnasco, M.O., Vosshall, L.B., Keller, A.: Humans can discriminate more than 1 trillion olfactory stimuli. Science 343, 1370–1372 (2014)
Isaksson, A., Wennberg, A., Zetterberg, L.: Computer analysis of EEG signals with parametric models. Proc. IEEE 69(4), 451–461 (1981)
Guger, C., Ramoser, H., Pfurtscheller, G.: Real-time EEG analysis with subject-specific spatial patterns for a brain-computer interface (BCI). IEEE Trans. Rehabil. Eng. 8(4), 447–456 (2000)
Tolonen, U., Sulg, I.: Comparison of quantitative EEG parameters from four different analysis techniques in evaluation of relationships between EEG and CBF in brain infarction. Electroencephalogr. Clin. Neurophysiol. 51(2), 177–185 (1981)
Gevins, A., Yeager, C., Zeitlin, G., Ancoli, S.: On-line computer rejection of EEG artifact. Electroencephalogr. Clin. Neurophysiol. 42(2), 267–274 (1979)
Wennberg, A., Zetterberg, L.: Application of a computer-based model for EEG analysis. Electroencephalogr. Clin. Neurophysiol. 31(5), 457–468 (1971)
Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non stationary time series analysis. Proc. R. Soc. A Math. Phys. Eng. Sci. 454(1971), 903–995 (1998)
Li, H., Yang, L., Huang, D.: Application of Hilbert Huang transform to heart rate variability analysis. In: The 2nd International Conference on Bioinformatics and Biomedical Engineering, pp. 648–651 (2008)
Zong, C., Chetouani, M.: Hilbert-Huang transform based physiological signals analysis for emotion recognition. In: IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) (2009)
Barnhart, B.L.: The Hilbert Huang: theory, applications, development. University of Iowa, Iowa Research Online. http://ir.uiowa.edu/etd/2670
Acknowledgments
This work was supported by the statutory funds of the Department of Systems and Computer Networks, Wroclaw University of Science and Technology.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Puchala, E., Krysmann, M. (2018). An Algorithm for Detecting the Instant of Olfactory Stimulus Perception, Using the EEG Signal and the Hilbert-Huang Transform. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds) Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. CORES 2017. Advances in Intelligent Systems and Computing, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-319-59162-9_52
Download citation
DOI: https://doi.org/10.1007/978-3-319-59162-9_52
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59161-2
Online ISBN: 978-3-319-59162-9
eBook Packages: EngineeringEngineering (R0)