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Explainable Machine Learning Methods for Classification of Brain States during Visual Perception

Author

Listed:
  • Robiul Islam

    (Laboratory of Neuroscience and Cognitive Technology, Innopolis University, 1 Universitetskaya Str., 420500 Innopolis, Russia)

  • Andrey V. Andreev

    (Laboratory of Neuroscience and Cognitive Technology, Innopolis University, 1 Universitetskaya Str., 420500 Innopolis, Russia
    Baltic Center of Neurotechnology and Artificial Intelligence, Immanuil Kant Baltic Federal University, 14 A. Nevskogo ul., 236041 Kaliningrad, Russia)

  • Natalia N. Shusharina

    (Baltic Center of Neurotechnology and Artificial Intelligence, Immanuil Kant Baltic Federal University, 14 A. Nevskogo ul., 236041 Kaliningrad, Russia)

  • Alexander E. Hramov

    (Baltic Center of Neurotechnology and Artificial Intelligence, Immanuil Kant Baltic Federal University, 14 A. Nevskogo ul., 236041 Kaliningrad, Russia
    Department of Mechanical Engineering and Instrumentation, Engineering Academy, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya St., 117198 Moscow, Russia)

Abstract

The aim of this work is to find a good mathematical model for the classification of brain states during visual perception with a focus on the interpretability of the results. To achieve it, we use the deep learning models with different activation functions and optimization methods for their comparison and find the best model for the considered dataset of 31 EEG channels trials. To estimate the influence of different features on the classification process and make the method more interpretable, we use the SHAP library technique. We find that the best optimization method is Adagrad and the worst one is FTRL. In addition, we find that only Adagrad works well for both linear and tangent models. The results could be useful for EEG-based brain–computer interfaces (BCIs) in part for choosing the appropriate machine learning methods and features for the correct training of the BCI intelligent system.

Suggested Citation

  • Robiul Islam & Andrey V. Andreev & Natalia N. Shusharina & Alexander E. Hramov, 2022. "Explainable Machine Learning Methods for Classification of Brain States during Visual Perception," Mathematics, MDPI, vol. 10(15), pages 1-25, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2819-:d:883322
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    References listed on IDEAS

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    1. Stan Lipovetsky & Michael Conklin, 2001. "Analysis of regression in game theory approach," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 17(4), pages 319-330, October.
    2. Muratova, Anna & Islam, Robiul & Mitrofanova, Ekaterina S. & Ignatov, Dmitry I., 2019. "Searching for Interpretable Demographic Patterns," MPRA Paper 97305, University Library of Munich, Germany, revised 23 Sep 2019.
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