004.021:004.77

neurointerface, cassification, signal processing, comparative analysis, EEG.

The market of modern neurointerfaces, despite its active development, unfortunately, can offer users only a number of existing prototypes that have a relatively low accuracy and identification reliability of the human operator control effects. In addition, any neurointerface on the market must be individually tailored to each operator, which makes it difficult to objectify its accuracy, precision and reliability. The first step in solving the above problems is to conduct a comparative analysis of different price segments of the market of existing neurointerface technologies, as presented in this article. The market research revealed that despite the disadvantages of electroencephalography, it is one of the most accessible non-invasive methods of recording biological signals in neurointerface systems. To facilitate future research, the main advantages and disadvantages of known models and methods of signal analysis in neurointerfaces have been considered and analyzed. In particular, in the context of signal pre-processing, advantages and disadvantages of such methods as Common Average Referencing, Independent Component Analysis, Common Spatial Patterns, Surface Laplacian, Common Spatio-Spatial Patterns and Adaptive Filtering are considered. At the stage of evaluating the informative characteristics of the signal, the analysis of models and methods based on the models of adaptive parameters of autoregression, bilinear autoregression, multidimensional autoregression, fast Fourier transform, wavelet transformation, wave packet decomposition is performed. Besides, a comparative analysis of the most common methods of identification (recognition) of control effects of the human neurointerface operator, namely, the method of discriminant analysis, the method of reference vectors, nonlinear Bayesian classifiers, classifiers of nearest neighbors, artificial neural networks is carried out. The study of neurointerface technologies provides researchers with additional grounds for a sound choice of mathematical, software and hardware of neurointerface systems, as well as contributes to the development of new versions with increased accuracy, reliability and reliability.

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1. Tufte, E. R. “Envisioning Information.” Cheshire, CT: Graphics Press, 1990.
2. Suchman, L. A. “Plans and situated actions: the problem of human-machine communication.” Cambridge University Press, Cambridge, UK 1987.
3. Lupenko S. A., Butsiy R. A. “Modern neurointerface technologies: actuality, prospects and complexities”, International Scientific and Technical Conference “Ivan Puluj: life in the name of science and Ukraine”, 2020. P. 81–82.
4. Shih J., Krusienski D., Wolpaw J., “Brain-Computer Interfaces in Medicine”. Mayo Clin Pro. Vol. 87. No. 3, 2012. P. 268–279.
5. Wolpaw J., McFarland D., “Control of a two-dimensional movement signal by a noninvasive brain–computer interface in humans”. Proc Natl Acad Sci. Vol. 101. No. 51. 2004.
6. ButsIy R., Lupenko C. AnalIz osnovnih harakteristik komertsIynih neyroInterfeysIv. IX MIzhnarodna naukovo-tehnIchna konferentsIya molodih uchenih ta studentIv “AktualnI zadachI suchasnih tehnologIy”, 25–26 listopada 2020 r.: tezi dop. TernopIl, 2020. Tom 2. Р. 9–10.
7. T. Kameswara Rao, M. Rajya Lakshmi, Dr. T. V. Prasad, “An Exploration of Brain Computer Interface and Its Recent Trends”, Int. J. of Advanced Research in Artificial Intelligence. Vol. 1. No. 8. 2012.
8. William O., Ellen R., “Grass Lecture: Extraordinary EEG”, Neurodiagnostic Journal. Vol. 54.2014. P. 3–21.
9. Logothetis N., Pauls J., Auguth M., Trinath T., Oeltermann A., “A neurophysiological investigation of the basis of the BOLD signal in fMRI”. Nature. Vol. 412. 2001. P. 150–157.
10. Changde Du., Changying Du., Huiguang He. “Sharing deep generative representation for perceived image reconstruction from human brain activity”. International Joint Conference on Neural Networks. 2017.
11. Aruna T., Vijay N., “Brain-computer interface: a thought translation device turning fantasy into reality”, Int. J. Biomedical Engg. and Tech. Vol. 11. No. 2. 2013.

5. ButsIy R., Lupenko C. AnalIz metodIv dlya zadach opratsyuvannya signalIv neyroInterfeysnih sistem. VIII Naukovo-tehnIchna konferentsIya “InformatsIynI modelI, sistemi ta tehnologIYi”, 9–10 grudnya 2020 r.: tezi dop. TernopIl, 2020. Р. 3.
6. D. J. McFarland, L. M. McCane, S. V. David, and J. R. Wolpaw, “Spatial filter selection for EEGbased communication”, Electroencephalogr. Clin. Neurophysiol. Vol. 103. No. 3.1997. P. 386–394.
7. Mohammed J., “Common Average Reference (CAR) Improves P300 Speller”, Int. J. of Engg. and Tech. Vol. 2. No. 3. 2012.
8. Arnaud D., Scott M., “EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis”. J. of Neuroscience Methods. Vol. 134. 2004.
9. Jung T., Makeig S. and Humphries S., “Extended ICA Removes Artefacts from Electroencephalographic Recording”, Advances in Neural Inf. Processing Systems. Cambridge. Vol. 10. 1998.
10. Koles J., Lazaret S., Zhou Z., “Spatial patterns underlying population differences in the background EEG”. Brain topography. Vol. 2. 1990. P. 275–284.
11. Fabien L. and Cuntai G., “Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms”. IEEE Trans. On Biomedical Engg. Vol. 58. No. 2. 2011.
12. Hjorth B., “An on-line transformation of EEG scalp potentials into orthogonal source derivations”, Electroencephalography and Clinical Neurophysiology. Vol. 2. 1975. P. 526–530.
13. Thakor N. and Zhu Y., “Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection”, IEEE Transactions on Biomedical Engineering. Vol. 38. 1991. P. 785–794.
14. Chandrakar C. and Kowar M., “De-noising ECG Signals Using Adaptive Filter Algorithm”, Int. J. of Soft Computing and Engg. Vol. 2. No 1. 2012.
15. AlMejrad S. “Human Emotions Detection using Brain Wave Signals”, European Journal of Scientific Research. Vol. 4. 2010.
16. Grossmann A. and Morlet J. “Decomposition of Hardsi functions into square integrable wavelets of constant shape”. SIAM J. Math. Vol. 15. 1984. P. 723–736.
17. Mallat S. “Multiresolution representations and wavelets”, Ph.D. Thesis, University of Pennsylvania, Philadelphia. 1988.
18. Einstein A. “On a Heuristic Viewpoint Concerning the Production and Transformation of Light”. Annalen der Physik. Vol. 17.1905. P. 132–148.
19. Wu T., Yan G., Yang B., Sun H. “EEG feature extraction based on wavelet packet decomposition for brain computer interface”. Elsevier. Vol. 41. 2008. P. 618–625.
20. Lotte F., Bougrain L., Cichocki A., Congedo M. “A A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update. Journal of Neural Engineering. Vol. 4. 2018.
21. McLachlan G., “Discriminant Analysis and Statistical Pattern Recognition”. New Jersey: Wiley Interscience, 2004.
22. Senthilmurugan M., Latha N., Malmurugan N., “Classification in EEG-Based Brain Computer Interfaces Using Inverse Model”. Int. J.of Computer Theory and Engg. Vol. 2. 2011.
23. Cortes C., Vapnik V. “Support-vector networks”, Machine Learning. Vol. 20. 1995. P. 273–297.
24. Garrett D., Peterson D., Anderson C., Thaut M. “Comparison of Linear, Nonlinear, and Feature Selection Methods for EEG Signal Classification”. IEEE Trans. on Neural Systems And Rehabilitation Engg. Vol. 11. No. 2. June 2003.
25. Lotte F. “Study of Electroencephalographic Signal Processing and Classification Techniques towards the use of Brain-Co–mputer Interfaces in Virtual Reality Applications”. Rennes. 2009.
26. Rajya Lakshmi M., Dr Prasad T. V., Dr Chandra V. “Prakash Survey on EEG Signal Processing Methods”, Int. J. of Advanced Research in Computer Science and Software Engineering. Vol. 4. 2014. P. 84–91.
27. Altman N. “An introduction to kernel and nearest-neighbor nonparametric regression”. The American Statistician. Vol. 46. 1992. P. 175–185.
28. Andreas Z. “Simulation of Neural Networks”. Tuebingen: Addison-Wesley, 1994.
29. Lupenko S., Lytvynenko I., Stadnyk N. (2020) Method for reducing the computational complexity of processing discrete cyclic random processes in digital data analysis systems. Scientific Journal of TNTU (Tern.). Vol. 97. No. 1. Р. 110–121.
30. Lupenko S. A., Osukhivska H. M., Lutsyk N. S., Stadnyk N. B., Zozulia A. M., Shablii N. R. (2016) The comparative analysis of mathematical models of cyclic signals structure and processes. Scientific Journal of TNTU (Tern.). Vol. 82. No. 2. Р. 115–127.