D.V. Zhuravlev1, A.N. Golubinsky2, A. A. Tolstykh3, A. A. Reznichenko4

1, 4 Voronezh State Technical University (Voronezh, Russia)
2 A.A. Harkevich Institute of Information Transmission Problems of the Russian Academy of Sciences (Moscow, Russia)
3 RTK LLC (Moscow, Russia)
1 ddom1@yandex.ru, 2 annikgol@mail.ru, 3 tolstykh.aa@yandex.ru, 4andrei.reznichencko2017@yandex.ru

Currently, there are no uniform recommendations for setting up both recording equipment and software to ensure the uniformity of measurements and increased accuracy of classifications in motor image recognition tasks. However, improving the accuracy and consistency of classifications is the main goal of all research in this field, regardless of the registration equipment and software used.

The purpose – development of a unified methodology for configuring the parameters of the brain-computer interfaces to ensure the uniformity of measurements and the maximum possible values of accuracy in the classification of motor images.
Recommendations are given on setting up the electroencephalogram recording equipment. To form recommendations on options for optimal localization of electrodes, as well as their number, computational experiments were conducted to calculate the accuracy of classification of motor images when processing signals received from various numbers of electrodes and their localizations. As a result of the experiments, it was found that when the reference electrode is located in the central part of the head, the number of pairs of electrodes to achieve the highest classification accuracy should be from 3 to 5 (respectively, these are 6-10 electrodes located symmetrically relative to the midline of the head). The influence of the frequency range of EEG registration on the classification accuracy was also investigated. As a result of the study, it was found that the maximum amplitude of the beta rhythm oscillations that desynchronize during the preparation and execution of the motor act are in the frequency band 19-31 Hz. However, this range strongly depends on the physiological properties of the subject and can be adjusted. The influence of three machine learning algorithms on the accuracy of classification has been studied. The optimal settings of the classifiers have been formed. The maximum average classification accuracy was 67.37% when using a classifier based on the linear discriminant analysis (LDA) method. The maximum peak value of classification accuracy was 74.90%. When using a third-party database, the average classification accuracy was 73.44%, which shows the correctness of the recommendations formulated in the methodology.

The developed technique will be useful to a wide range of researchers and developers of brain-computer interfaces due to the fact that setting up the equipment will take much less time. This will allow us to focus on the main task of research in this area – the creation of a universal classifier architecture that provides increased accuracy. The recommended technique for configuring neurocomplexes in MI recognition will allow you to achieve maximum classification accuracy, regardless of the type of electrodes, the type of registration equipment used and classification software.

Zhuravlev D.V., Golubinsky A.N., Tolstykh A.A., Reznichenko A.A. Development of a methodology for tuning parameters of brain-computer interfaces for conducting experiments on the classification of motor images in the OpenVIBE program. Biomedicine Radioengineering. 2025. V. 28. № 3. P. 15–30. DOI: https:// doi.org/10.18127/j15604136-202503-02 (In Russian)

1. Oficial'nyj sajt proekta OpenBCI. URL: https://openbci.com (data obrashcheniya: 11.09.2024).
2. Oficial'nyj sajt proekta OpenVIBE. URL: https://openvibe.inria.fr. (data obrashcheniya: 11.09.2024).
3. Renard Y., Lotte F., Gibert G., Congedo M., Maby E., Delannoy V., Bertrand O. Lécuyer A. OpenViBE: An Open-Source Software Platform to Design, Test and Use Brain-Computer Interfaces in Real and Virtual Environments. Presence Teleoperators & Virtual Environments. 2010. V. 19. № 1. P. 35–53.
4. Pfurtscheller G., Neuper C. Motor imagery and direct brain-computer communication. Proceedings of the IEEE. 2001. V. 89. № 1. P. 1123–1134.
5. Brodu N., Lotte F., Lécuyer A. Exploring Two Novel Features for EEG-based Brain-Computer Interfaces: Multifractal Cumulants and Predictive Complexity. Neurocomputing. 2010. V. 79. № 2. P. 87–94.
6. Ahirwal M., Londhe N. Offline Study of Brain Computer Interfacing for Hand Movement Using Open-VIBE. Proceedings of 2011 International Conference on Process Automation, Control and Computing (PACC 2011). 2011. P. 1–5.
7. Gaur P., Pachori R.B., Wang H., Prasad G. Enhanced motor imagery classification in EEG-BCI using multivariate EMD based filtering and CSP features. International Brain-Computer Interface (BCI) Meeting. 2016. P. 1–8.
8. Kirar J.S., Agrawal R.K. Relevant Feature Selection from a Combination of Spectral-Temporal and Spa-tial Features for Classification of Motor Imagery EEG. Journal of medical systems. 2018. V. 42. № 78. P. 1–15.
9. Guo Y., Zhang Y., Chen Z., Liu Yi., Chen W. EEG classification by filter band component regularized common spatial pattern for motor imagery. Biomedical Signal Processing and Control. 2020. V. 59.
10. Selim S., Tantawi M., Shedeed H., Badr A. A Comparative Analysis of Different Feature Extraction Tech-niques for Motor Imagery Based BCI System. Proceedings of the International Conference on Artificial Intelli-gence and Computer Vision (AICV2020). 2020. P. 740–749.
11. Kapralov N.V., Nagornova Zh.V., Shemyakina N.V. Metody klassifikacii EEG-patternov voobra-zhaemyh dvizhenij. Informatika i avtomatizaciya. 2021. T. 20. № 1. C. 94–133 (In Russian).
12. Kabir M., Mahmood S., Shiam A.A., Miah A.S. M., Shin J., Molla M.K. Investigating Feature Selection Techniques to Enhance the Performance of EEG-Based Motor Imagery Tasks Classification. Mathematics. 2023. V. 11. № 8. 1921. P. 1–19.
13. Wang J., Jiang W., Kim J. A novel ECG and EEG classification system based on nonlinear statistical fea-tures. Fractals. 2023. V. 31. № 07.
14. Nambiar A., Sivakumar R., Subramaniam S. Detection of Four Class Motor Imagery from EEG Signal for Brain-Computer Interface Applications. Applied Mathematics, Modeling and Computer Simulation. 2023. V. 42. P. 881–894.
15. Kabir M.H., Akhtar N.I., Tasnim N., Miah A.S.M., Lee H.-S., Jang S.-W., Shin J. Exploring Feature Se-lection and Classification Techniques to Improve the Performance of an Electroencephalography-Based Motor Imagery Brain-Computer Interface System. Sensors. 2024. V. 24. № 15. P. 4989.
16. Lu Y., Wang W., Lian B., He C. Feature Extraction and Classification of Motor Imagery EEG Signals in Motor Imagery for Sustainable Brain–Computer Interfaces. Sustainability. 2024. V. 16. № 15. P. 6627.
17. Tayeb Z., Fedjaev J., Ghaboosi N., Richter C., Everding L., Qu X., Wu Y., Cheng G., Conradt J. Validat-ing Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals. Sensors. 2019. V. 19. № 1. P. 210.
18. Kurkin S.A., Picik E.N., Hramov A.E. Ispol'zovanie iskusstvennyh nejronnyh setej dlya klas-sifikacii elektricheskoj aktivnosti golovnogo mozga v processe voobrazheniya dvizhenij u netreni-rovannyh ispytuemyh. Informacionno-upravlyayushchie sistemy. 2019. № 6.
    C. 77–84 (In Russian).
19. Mane R., Robinson N., Vinod A.P., Lee S.-W., Guan C. A Multi-view CNN with Novel Variance Layer for Motor Imagery Brain Computer Interface. 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2020. P. 2950–2953.
20. Echtioui A., Mlaouah A., Zouch W., Ghorbel M., Mhiri C., Hamam H. A Novel Convolutional Neural Network Classification Approach of Motor-Imagery EEG Recording Based on Deep Learning. Appl. Sci. 2021. V. 11. № 21. P. 9948.
21. Uyulan C. Development of LSTM&CNN based hybrid deep learning model to classify motor imagery tasks. Communications in Mathematical Biology and Neuroscience. 2021. Article ID. 4. P. 1–26.
22. Bafana S. Applying Dimensionality Reduction as Precursor to LSTM-CNN Models for Classifying Im-agery and Motor Signals in ECoG-Based BCIs. arXiv.org. 2023. URL: http://arxiv.org/abs/2311.13507 (data obrashcheniya: 17.09.2024).
23. Aung H. W., Li J. J., An Y., Su S. W. EEG_GLT-Net: Optimising EEG Graphs for Real-time Motor Image-ry Signals Classification. arXiv.org. 2024. URL: http://arxiv.org/abs/2404.11075 (data obrashcheniya: 17.09.2024).
24. Aung H. W., Li J. J., An Y., Su S. W. EEG_RL-Net: Enhancing EEG MI Classification through Rein-forcement Learning-Optimised Graph Neural Networks. arXiv.org. 2024. URL: http://arxiv.org/abs/2405.00723 (data obrashcheniya: 17.09.2024).
25. Han J., Wei X., Faisal A. A. EEG decoding for datasets with heterogenous electrode configurations using transfer learning graph neural networks. arXiv.org. 2023. URL: http://arxiv.org/abs/2306.13109 (data obrashcheniya: 17.09.2024).
26. Xiong X., Su L., Huang J., Kang G. Enhancing Motor Imagery Decoding in Brain Computer Interfaces us-ing Riemann Tangent Space Mapping and Cross Frequency Coupling. arXiv.org. 2023. URL: http://arxiv.org/abs/2310.19198 (data obrashcheniya: 18.09.2024).
27. Ai Y., Rajendran B. A Convolutional Spiking Network for Gesture Recognition in Brain-Computer Inter-faces. arXiv.org. 2023. URL: http://arxiv.org/abs/2304.11106 (data obrashcheniya: 19.09.2024).
28. Salami A., Andreu-Perez J., Gillmeister H. EEG-ITNet: An Explainable Inception Temporal Convolution-al Network for Motor Imagery Classification. arXiv.org. 2023. URL: http://arxiv.org/abs/2204.06947 (data obrashcheniya: 19.09.2024)
29. Lopez-Bernal D., Balderas D., Ponce P., Molina A. Exploring inter-trial coherence for inner speech clas-sification in EEG-based brain-computer interface. Journal of Neural Engineering. 2024. V. 21. № 2. P. 1–14.
30. Xue K., Yang J., Yao F. Optimal Linear Discriminant Analysis for High-Dimensional Functional Data. Journal of the American Statistical Association. 2023. V. 119. P. 1055–1064.
31. Le C.V. A Comment on Hansen’s Risk of James-Stein and Lasso Shrinkage. Prediction and Causality in Econometrics and Related Topics. 2021. P. 434–445.
32. Gupta D., Gupta U., Sarma H. Functional iterative approach for Universum-based primal twin bounded support vector machine to EEG classification (FUPTBSVM). Multimedia Tools and Applications. 2023. V. 83. № 8. P. 1–33.
33. Hazarika D., Gupta D., Kumar B. EEG Signal Classification Using a Novel Universum-Based Twin Par-ametric-Margin Support Vector Machine. Cognitive Computation. 2023. V. 16. P. 2047–2062.
34. Ganaie M., Tanveer M., Jangir J. EEG signal classification via pinball universum twin support vector machine. Annals of Operations Research. 2022. V. 328. P. 451–492.
35. Ferreira S., Silveira A., Pereira A. EEG-Based Motor Imagery Classification Using Multilayer Percep-tron Neural Network. Proceedings of the XXVII Brazilian Congress on Biomedical Engineering (CBEB 2020). 2022. P. 1873–1878.
36. Li P., Liu Y., Cai W., Liu X. MAC: Epilepsy eeg signal recognition based on the mlp-self-attention model and cosine distance. Journal of Mechanics in Medicine and Biology. 2024. V. 24. № 2. P. 1–17.
37. Halnes G., Ness T.V., Næss S., Hagen E., Pettersen K.H., Einevoll G.T. Electric Brain Signals: Founda-tions and Applications of Biophysical Modeling. Cambridge University Press. 2024. 379 p.