Multiparameter Analysis of Statistical Memory Effects in Bioelectric Signals while Performing Cognitive Tasks

Valentin A. Yunusov orcid (Login required)
Kazan Federal University, Russia

Sergey A. Demin orcid
Kazan Federal University, Russia


Paper #8968 received 2 May 2023; revised manuscript received 22 Jun 2023; accepted for publication 2 Jul 2023; published online 6 Nov 2023.

DOI: 10.18287/JBPE23.09.040302

Abstract

In this research, in the framework of Memory Functions Formalism, we study statistical memory effects of electroencephalogram data for two groups of people by performing auto- and cross-correlation analysis. The first group consists of 8 professional musicians; the second group was represented by 11 people without any musical education. Bioelectrical activity signals were recorded during 2 cognitive tasks: perceiving a fragment of musical piece, and perceiving a text read aloud. During autocorrelation analysis, we identify regions of brain cortex, statistical memory effects of signals from which differ the most and use them for the following analysis. During the second stage of work, we identify differences in spectral behavior for both groups and analyze the effects of frequency-phase synchronization. Finally, it is demonstrated that our approach allows detecting differences in the cognitive abilities of people when performing various cognitive task.

Keywords

data science; living systems; biomedical data; time series analysis; autocorrelations; cross-correlations; cognition; electroencephalograms

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References


1. B. Paulo, M. David, “Efficiency of attentional networks in musicians and non-musicians,” Heliyon 5, 1–17 (2019).

2. N. Shourie, S. M. P. Firoozabadi, and K. Badie, “Investigation of EEG Alpha Rhythm of Artists and Nonartists During Visual Perception, Mental Imagery, and Rest,” Journal of Neurotherapy 17, 166–177 (2013).

3. V. Yunusov, S. Demin, “The analysis of local correlation characteristics of human bioelectric signals while performing cognitive tasks,” in 2022 VIII International Conference on Information Technology and Nanotechnology (ITNT), IEEE, 1–4 (2022).

4. M. C. Cheung, A. S. Chan, Y. Liu, D. Law, and C. W. Y. Wong, “Music training is associated with cortical synchronization reflected in EEG coherence during verbal memory encoding,” PLoS One 12(3), 0174906 (2017).

5. S. Liang, T. Hsieh, and W. Chen, “Classification of EEG signals from musicians and nonmusicians by neural networks,” IEEE 2011 9th World Congress on Intelligent Control and Automation, 865–869 (2011).

6. I. Sturm, S. Dähne, B. Blankertz, and G. Curio, “Multi-Variate EEG Analysis as a Novel Tool to Examine Brain Responses to Naturalistic Music Stimuli,” PLoS One 10(10), e0141281 (2015).

7. E. Ribeiro, C. E. Thomas, “A multivariate statistical analysis of EEG signals for differentiation of musicians and non-musicians,” Proceedings of the 15th National Meeting on Artificial and Computational Intelligence, 497–505 (2018).

8. S. A. Demin, R. M. Yulmetyev, O. Y. Panischev, and P. Hänggi, “Statistical quantifiers of memory for an analysis of human brain and neuro-system diseases,” Statistical Mechanics and its Applications 387(8–9), 2100–2110 (2008).

9. V. Yunusov, S. A. Demin, “The development of statistical methods for the diagnosis of neurological diseases based on multiparameter analysis of brain activity,” Journal of Physics: Conference Series 1740(1), 012049 (2021).

10. O. Y. Panischev, S. A. Demin, and J. Bhattacharya, “Cross-correlation markers in stochastic dynamics of complex systems,” Physica A: Statistical Mechanics and its Applications 389(21), 4958–4969 (2010).

11. J. Bhattacharya, H. Petsche, “Phase synchrony analysis of EEG during music perception reveals changes in functional connectivity due to musical expertise,” Signal Processing 85(11), 2161–2177 (2005).

12. S. Karkare, G. Saha, and J. Bhattacharya, “Investigating long-range correlation properties in EEG during complex cog-nitive tasks,” Chaos, Solitions & Fractals 42(4), 2067–2073 (2009).

13. R. Batt, M. Palmiero, C. Nakatani, and C. van Leeuwen, “Style and Spectral Power: Processing of Abstract and Representation Art in Artists and Non-Artists,” Perception 39(12), 1659–1671 (2010).

14. S. Koelsch, T. Gunter, A. D. Friederici, and E. Schröger, “Brain indices of music processing: “nonmusicians” are musicals,” Journal of Cognitive Neuroscience 12(3), 520–541 (2000).

15. P. Saari, I. Burunat, E. Brattico, and P. Toiviainen, “Decoding Musical Training from Dynamic Processing of Musical Features in the Brain,” Journal of Cognitive Neuroscience 8, 708–719 (2017).

16. R. Nawaz, H. Nisar, and Y. V. Voon, “The Effect of Music on Human Brain; Frequency Domain and Time Series Analysis Using Electroencephalogram,” IEEE Access 6, 45191–45205 (2018).

17. S. Demin, O. Panischev, V. Yunusov, and S. Timashev, “The use of Flicker-Noise Spectroscopy in the diagnosis of photosensitive epilepsy based on the analysis of human magnetoencephalograms,” in 2020 International Conference on Information Technology and Nanotechnology (ITNT), IEEE, 1–4 (2020).

18. S. A. Demin, V. A. Yunusov, “Flicker-noise spectroscopy analysis of magnetoencephalogram signals in diagnosis and treatment of photosensitive epilepsy,” Progress in Biomedical Optics and Imaging – Proceedings of SPIE 12194, 1219402 (2022).

19. K. V. Saboo, Y. Varatharajah, B. M. Berry, V. Kremen, M. R. Sperling, K. A. Davis, B. C. Jobst, R. E. Gross, B. Lega, S. A. Sheth, G. A. Worrell, R. K. Iyer, and M. T. Kucewicz, “Unsupervised machine-learning classification of electrophysiologically active electrodes during human cognitive task perfor-mance,” Scientific Reports 9(1), 17390 (2019).

20. V. A. Yunusov, S. A. Demin, and A. A. Elenev, “The study of statistical features of the evolution of complex physical systems using adaptive machine learning methods,” Journal of Physics: Conference Series 2270(1), 012042 (2022).




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