IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i15p2819-d883322.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/15/2819/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/15/2819/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Borgonovo, Emanuele & Plischke, Elmar & Rabitti, Giovanni, 2024. "The many Shapley values for explainable artificial intelligence: A sensitivity analysis perspective," European Journal of Operational Research, Elsevier, vol. 318(3), pages 911-926.
    2. Pera, Rebecca & Viglia, Giampaolo & Furlan, Roberto, 2016. "Who Am I? How Compelling Self-storytelling Builds Digital Personal Reputation," Journal of Interactive Marketing, Elsevier, vol. 35(C), pages 44-55.
    3. Stan Lipovetsky, 2021. "Predictor Analysis in Group Decision Making," Stats, MDPI, vol. 4(1), pages 1-14, February.
    4. Hugh Chen & Scott M. Lundberg & Su-In Lee, 2022. "Explaining a series of models by propagating Shapley values," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    5. Emrah Arbak, 2017. "Identifying the provisioning policies of Belgian banks," Working Paper Research 326, National Bank of Belgium.
    6. Viglia, Giampaolo & Abrate, Graziano, 2017. "When distinction does not pay off - Investigating the determinants of European agritourism prices," Journal of Business Research, Elsevier, vol. 80(C), pages 45-52.
    7. Xingwei Hu, 2020. "A theory of dichotomous valuation with applications to variable selection," Econometric Reviews, Taylor & Francis Journals, vol. 39(10), pages 1075-1099, November.
    8. Dmitry Sharapov & Paul Kattuman & Diego Rodriguez & F. Javier Velazquez, 2021. "Using the SHAPLEY value approach to variance decomposition in strategy research: Diversification, internationalization, and corporate group effects on affiliate profitability," Strategic Management Journal, Wiley Blackwell, vol. 42(3), pages 608-623, March.
    9. Xingwei Hu, 2018. "A Theory of Dichotomous Valuation with Applications to Variable Selection," Papers 1808.00131, arXiv.org, revised Mar 2020.
    10. Filotto, Umberto & Caratelli, Massimo & Fornezza, Fabrizio, 2021. "Shaping the digital transformation of the retail banking industry. Empirical evidence from Italy," European Management Journal, Elsevier, vol. 39(3), pages 366-375.
    11. Elena Pokryshevskaya & Evgeny Antipov, 2013. "Importance-performance analysis for internet stores: a system based on publicly available panel data," HSE Working papers WP BRP 08/MAN/2013, National Research University Higher School of Economics.
    12. Pelin Ayranci & Phung Lai & Nhathai Phan & Han Hu & Alexander Kolinowski & David Newman & Deijing Dou, 2022. "OnML: an ontology-based approach for interpretable machine learning," Journal of Combinatorial Optimization, Springer, vol. 44(1), pages 770-793, August.
    13. Eranga M. Wimalasiri & Ebrahim Jahanshiri & Tengku Adhwa Syaherah Tengku Mohd Suhairi & Hasika Udayangani & Ranjith B. Mapa & Asha S. Karunaratne & Lal P. Vidhanarachchi & Sayed N. Azam-Ali, 2020. "Basic Soil Data Requirements for Process-Based Crop Models as a Basis for Crop Diversification," Sustainability, MDPI, vol. 12(18), pages 1-20, September.
    14. Khoa Tran & Hai-Canh Vu & Lam Pham & Nassim Boudaoud & Ho-Si-Hung Nguyen, 2024. "Robust-MBDL: A Robust Multi-Branch Deep-Learning-Based Model for Remaining Useful Life Prediction of Rotating Machines," Mathematics, MDPI, vol. 12(10), pages 1-25, May.
    15. Gi-Wook Cha & Choon-Wook Park & Young-Chan Kim & Hyeun Jun Moon, 2023. "Predicting Generation of Different Demolition Waste Types Using Simple Artificial Neural Networks," Sustainability, MDPI, vol. 15(23), pages 1-22, November.
    16. Gabriel Ferrettini & Elodie Escriva & Julien Aligon & Jean-Baptiste Excoffier & Chantal Soulé-Dupuy, 2022. "Coalitional Strategies for Efficient Individual Prediction Explanation," Information Systems Frontiers, Springer, vol. 24(1), pages 49-75, February.
    17. Riccardo Colini-Baldeschi & Marco Scarsini & Stefano Vaccari, 2018. "Variance Allocation and Shapley Value," Methodology and Computing in Applied Probability, Springer, vol. 20(3), pages 919-933, September.
    18. Antoniadis, Anestis & Lambert-Lacroix, Sophie & Poggi, Jean-Michel, 2021. "Random forests for global sensitivity analysis: A selective review," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    19. Haim Shalit, 2020. "The Shapley value of regression portfolios," Journal of Asset Management, Palgrave Macmillan, vol. 21(6), pages 506-512, October.
    20. Beltrán, Sergio & Castro, Alain & Irizar, Ion & Naveran, Gorka & Yeregui, Imanol, 2022. "Framework for collaborative intelligence in forecasting day-ahead electricity price," Applied Energy, Elsevier, vol. 306(PA).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2819-:d:883322. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.