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World Congress on Medical Physics and Biomedical Engineering 2018 pp 71–75Cite as

Functional State Assessment of an Athlete by Means of the Brain-Computer Interface Multimodal Metrics

Part of the IFMBE Proceedings book series (IFMBE,volume 68/3)

Abstract

The estimation in real time of the functional and mental state level for the athlete during the loads is essential for management of the training process. New multimodal metric, obtained by means of the brain-computer interface (BCI), is proposed. The paper discusses the results of the joint usage of data from Emotiv EPOC+ mobile wireless headset. It includes motion sensors (accelerometer) and EEG channels. The features of the Emotiv EPOC+ interface allow to record the deviation of the head from the body axis, which provides an additional channel of information about the physical and mental (psycho-emotional) state of the athlete. Based on this data a new multimodal metric is calculated. Approbation of the metric was performed for functional stress studies on group of 10 volunteer subjects, including evaluations of the TOVA-test and the hyperventilation load. The joint application of different signals modalities allows to obtain estimates level of attention for these functional studies.

Keywords

  • Brain-computer interface
  • Functional study
  • Multimodal interaction

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References

  1. Silva, H.P. da et al.: Biosignals for Everyone. IEEE Pervasive Comput. 13, 4, 64–71 (2014).

    Google Scholar 

  2. Mcdowell, K. et al.: Real-world neuroimaging technologies. IEEE Access. 1, 131–149 (2013).

    Google Scholar 

  3. Borisov, V. et al.: Mobile brain-computer interface application for mental status evaluation. In: 2017 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON). pp. 550–555 (2017).

    Google Scholar 

  4. David, H. et al.: Usability of four commercially-oriented EEG systems. J. Neural Eng. 11, 4, 046018 (2014).

    Google Scholar 

  5. Ries, A. et al.: A Comparison of Electroencephalography Signals Acquired from Conventional and Mobile Systems. (2014).

    Google Scholar 

  6. Wu, M. et al.: Modeling perceived stress via HRV and accelerometer sensor streams. Presented at the Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2015).

    Google Scholar 

  7. Danilov, Y.P. et al.: Vestibular sensory substitution using tongue electrotactile display. In: Human Haptic Perception: Basics and Applications. pp. 467–480 Birkhäuser Basel (2008).

    Google Scholar 

  8. Alexey Syskov et al.: Feature Extraction and Selection for EEG and Motion Data in Tasks of the Mental Status Assessing. Presented at the BIOSTEC 2018: 11th International Joint Conference on Biomedical Engineering Systems and Technologies (2018).

    Google Scholar 

  9. Dumas, B. et al.: Multimodal interfaces: A survey of principles, models and frameworks. Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma. 5440 LNCS, 3–26 (2009).

    Google Scholar 

  10. Syskov, A.M. et al.: Intelligent multimodal user interface for telemedicine application. In: 2017 25th Telecommunication Forum (℡FOR). pp. 1–4 (2017).

    Google Scholar 

  11. Mueller, S.T., Piper, B.J.: The Psychology Experiment Building Language (PEBL) and PEBL Test Battery. J. Neurosci. Methods. 222, 250–259 (2014).

    Google Scholar 

  12. N.I. Shlyk, R.M.B.: Rhythm of the heart and type of vegetative regulation in assessing the level of health of the population and the functional preparedness of athletes. Udmurt University, Izhevsk (2016).

    Google Scholar 

  13. Mathie, M.: Monitoring and Interpreting Human Movement Patterns Using a Triaxial Accelerometer. (2003).

    Google Scholar 

  14. Jolliffe, I.: Principal Component Analysis. In: Wiley StatsRef: Statistics Reference Online. John Wiley & Sons, Ltd (2014).

    Google Scholar 

  15. Wolpaw, J., Wolpaw, E.W.: Brain-Computer Interfaces: Principles and Practice. Oxford University Press, USA (2012).

    Google Scholar 

  16. McLachlan, G.J.: Discriminant Analysis and Statistical Pattern Recognition: McLachlan/Discriminant Analysis & Pattern Recog. John Wiley & Sons, Inc., Hoboken, NJ, USA (1992).

    Google Scholar 

  17. Kublanov, V.S. et al.: Classification of the physical training level by heart rate variability and stabilography data. In: 2017 Siberian Symposium on Data Science and Engineering (SSDSE). pp. 49–54 (2017).

    Google Scholar 

  18. Machado, I.P. et al.: Human activity data discovery from triaxial accelerometer sensor: Non-supervised learning sensitivity to feature extraction parametrization. Inf. Process. Manag. 51, 2, 201–214 (2015).

    Google Scholar 

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Acknowledgements

The work was supported by Act 211 Government of the Russian Federation, contract № 02.A03.21.0006.

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Authors and Affiliations

  1. Ural Federal University, Yekaterinburg, 620002, Russian Federation

    Vasilii Borisov, Alexey Syskov & Vladimir Kublanov

Authors
  1. Vasilii Borisov
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  2. Alexey Syskov
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  3. Vladimir Kublanov
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Corresponding author

Correspondence to Vasilii Borisov .

Editor information

Editors and Affiliations

  1. CIIRC, Czech Technical University in Prague, Prague, Czech Republic

    Dr. Lenka Lhotska

  2. Institute of Clinical and Experimental Medicine, Prague, Czech Republic

    Dr. Lucie Sukupova

  3. Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia

    Prof. Dr. Igor Lacković

  4. Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA

    Dr. Geoffrey S. Ibbott

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Borisov, V., Syskov, A., Kublanov, V. (2019). Functional State Assessment of an Athlete by Means of the Brain-Computer Interface Multimodal Metrics. In: Lhotska, L., Sukupova, L., Lacković, I., Ibbott, G. (eds) World Congress on Medical Physics and Biomedical Engineering 2018. IFMBE Proceedings, vol 68/3. Springer, Singapore. https://doi.org/10.1007/978-981-10-9023-3_13

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  • DOI: https://doi.org/10.1007/978-981-10-9023-3_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-9022-6

  • Online ISBN: 978-981-10-9023-3

  • eBook Packages: EngineeringEngineering (R0)

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