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Single Trial EEG Classification of Tasks with Dominance of Mental and Sensory Attention with Deep Learning Approach

  • Irina Knyazeva
  • Alexander Efitorov
  • Yulia Boytsova
  • Sergey Danko
  • Vladimir Shiroky
  • Nikolay Makarenko
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 799)

Abstract

In this paper, we present classification algorithms based on single-trial ElectroEncephaloGraphy (EEG) during the performance of tasks with the dominance of mental and sensory attention. Statistical data analysis showed numerous significant differences of EEG wavelet spectra density during this task at the group level. We decided to use wavelet power spectral density (PSD) computed in each channel for single trial as the source of feature extraction for the classification task. To obtain a low-dimensional representation of PSD image convolutional autoencoder (CNN) was trained. With this encoded representation binary classification for each subject with multilayer perceptron (MLP) were performed. The classification error varies depending on the subject with the average true classification rate is 83.4%, and the standard deviation is 6.6%. So this approach potentially could be used in the tasks where pattern classification is used, such as a clinical decision or in Brain-Computer Interface (BCI) system.

Keywords

Mental and sensory attention EEG single trial classification Deep learning Neural networks 

Notes

Acknowledgement

We gratefully acknowledge financial support of Institute of Information and Computational Technologies (Grant AR05134227, Kazakhstan)).

This study has been performed at the expense of the grant of Russian Science Foundation (project no. 18-11-00336).

References

  1. 1.
    Cohen, M.: Analyzing Time Series Data. MIT press, Cambridge, MA (2014)Google Scholar
  2. 2.
    Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for EEG–based brain–computer interfaces. J. Neural Eng. 4(2), R1 (2007)CrossRefGoogle Scholar
  3. 3.
    Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., Yger, F.: A review of classification algorithms for EEG–based brain–computer interfaces. J. Neural Eng. 15(3), 031005 (2018)CrossRefGoogle Scholar
  4. 4.
    Ray, W.J., Cole, H.W.: EEG alpha activity reflects attentional demands, and beta activity reflects emotional and cognitive processes. Science 228(4700), 750 (1985).  https://doi.org/10.1126/science.3992243CrossRefGoogle Scholar
  5. 5.
    Harmony, T., Fernández, T., Silva, J., Bernal, J., Díaz-Comas, L., Reyes, A., Marosi, E., Rodríguez, M., Rodríguez, M.: EEG delta activity: an indicator of attention to internal processing during performance of mental tasks. Int. J. Psychophysiol. 24(1–2), 161 (1996).  https://doi.org/10.1016/S0167-8760(96)00053-0CrossRefGoogle Scholar
  6. 6.
    Efitorov, A., Knyazeva, I., Boytsova, Y., Danko, S.: GPU-based high-performance computing of multichannel EEG phase wavelet synchronization. Procedia Comput. Sci. 123, 128 (2018).  https://doi.org/10.1016/J.PROCS.2018.01.021CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Irina Knyazeva
    • 1
    • 2
    • 3
  • Alexander Efitorov
    • 4
  • Yulia Boytsova
    • 5
  • Sergey Danko
    • 5
  • Vladimir Shiroky
    • 4
    • 6
  • Nikolay Makarenko
    • 1
    • 2
  1. 1.Pulkovo ObservatorySaint-PetersburgRussia
  2. 2.Saint-Petersburg State UniversitySaint-PetersburgRussia
  3. 3.Institute of Information and Computational TechnologiesAlmatyKazakhstan
  4. 4.Skobeltsyn institute of nuclear physicsLomonosov Moscow State UniversityMoscowRussia
  5. 5.Institute of the Human BrainRussian Academy of Sciences, St. PetersburgSaint-PetersburgRussia
  6. 6.National Research Nuclear University MEPhIMoscowRussia

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