Investigation of wave processes and rhythmic activity of the human brain using the Walsh orthogonal function system

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Abstract

The purpose of this work is to study the wave processes and rhythmic activity of the brain based on multiscale parametric maps of electroencephalograms obtained as a result of algorithmic application of a system of discrete functions. Methods. For visualization, a previously developed multi-scale method for constructing parametric mappings of molecular genetic information was used, in which a set of four nucleotides is considered as a system of orthogonal Walsh functions. Results. The article proposes a new method of visualization of electroencephalography data for the study of rhythmic and wave processes of bioelectric activity of the brain. To analyze the electroencephalography data, the stage of transcoding the recorded amplitudes was previously carried out by one-to-one conversion of the EEG signal into a symbolic sequence, the alphabet of which consisted of four characters. Based on this method, the EEG signals of the subject were compared at rest and under mental stress. The study analyzed the readings of electrodes registering biopotentials of the frontal lobes of the brain. Conclusion. New methods have made it possible to identify various configurations of clusters in the frequency space of visualization, which can be used for comparative analysis of encephalograms and identification of features of recorded EEG signals. Specialized software has been developed as a tool for studying the rhythmic activity of the brain by constructing parametric displays of electroencephalograms.  

About the authors

Ivan Viktorovich Stepanyan

Blagonravov Mechanical Engineering Research Institute of RAS

ORCID iD: 0000-0003-3176-5279
SPIN-code: 5644-6735
4, M. Kharitonyevskiy Pereulok, 101990 Moscow, the Russian Federation

Mikhail Yuryevich Lednev

Blagonravov Mechanical Engineering Research Institute of RAS

ORCID iD: 0000-0002-5919-0190
SPIN-code: 4277-6912
Scopus Author ID: 57216152928
ResearcherId: AAH-1782-2022
4, M. Kharitonyevskiy Pereulok, 101990 Moscow, the Russian Federation

References

  1. Ke J., Du J., Luo X. The effect of noise content and level on cognitive performance measured by electroencephalography (EEG) // Automation in Construction. 2021. Vol. 130. P. 103836. doi: 10.1016/j.autcon.2021.103836.
  2. Prasanna J. P., Subathra M. S. P., Mohammed M. A., Maashi M. S., Garcia-Zapirain B., Sairamya N. J., George S. T. Detection of focal and non-focal electroencephalogram signals using fast Walsh-Hadamard transform and artificial neural network // Sensors. 2020. Vol. 20, no. 17. P. 4952. doi: 10.3390/s20174952.
  3. Goker H., Tosun M. Fast Walsh–Hadamard transform and deep learning approach for diagnosing psychiatric diseases from electroencephalography (EEG) signals // Neural Comput. Applic. 2023. Vol. 35. P. 23617–23630. doi: 10.1007/s00521-023-08971-6.
  4. Goshvarpour A., Goshvarpour A. Analytic representation vs. angle modulation of Hilbert transform of fast Walsh-Hadamard coefficients (HTFWHC) in epileptic EEG classification // Braz. J. Phys. 2023. Vol. 53. P. 15. doi: 10.1007/s13538-022-01231-3.
  5. Mohsen S., Ghoneim S. S. M., Alzaidi M. S., Alzahrani A., Ali Hassan A. M. Classification of electroencephalogram signals using LSTM and SVM based on fast walsh-hadamard transform // Comput. Mater. Contin. 2023. Vol. 75, no. 3. P. 5271–5286. doi: 10.32604/cmc.2023.038758.
  6. Shakya N., Dubey R., Shrivastava L. Stress detection using EEG signal based on fast Walsh Hadamard transform and voting classifier // Preprint Research Square. 2021. doi: 10.21203/rs.3.rs- 782483/v1.
  7. Ergun E., Aydemir O. A hybrid BCI using singular value decomposition values of the fast walsh-hadamard transform coefficients // IEEE Transactions on Cognitive and Developmental Systems. 2020. Vol. 15, no. 2. P. 454–463. doi: 10.1109/TCDS.2020.3028785.
  8. Yuan X., Cai Z. A generalized Walsh system and its fast algorithm // IEEE Transactions on Signal Processing. 2021. Vol. 69. P. 5222–5233. doi: 10.1109/TSP.2021.3099635.
  9. Widdess-Walsh P. Resting but not idle: Insights into epilepsy network suppression from intracranial EEG // Epilepsy Currents. 2024. Vol. 24, no. 1. P. 25–27. doi: 10.1177/1535759723 1213247.
  10. Vaithialingam B., Rudrappa S. Intraoperative visualisation of 3 Hz spike–wave epileptic discharges in the electroencephalographic signal of bispectral index monitor in a patient with absence seizures // Indian J. Anaesth. 2024. Vol. 68, no. 2. P. 209–210. doi: 10.4103/ija.ija_710_23.
  11. Salami A., Andreu-Perez J., Gillmeister H. Finding neural correlates of depersonalisation/ derealisation disorder via explainable CNN-based analysis guided by clinical assessment scores // Artif. Intell. Med. 2024. Vol. 149. P. 102755. doi: 10.1016/j.artmed.2023.102755.
  12. Taylor J. A., Garrido M. I. Porthole and Stormcloud: tools for visualisation of spatiotemporal M/EEG statistics // Neuroinformatics. 2020. Vol. 18. no. 3. P. 351–363. doi: 10.1007/s12021- 019-09447-6.
  13. QiHan P. W., Alipal J., Suberi A. A. M., Fuad N., Wahab M. H. A., Idrus S. Z. S. A new perspective on visualising EEG signal of post-stroke patients // IOP Conf. Ser.: Mater. Sci. Eng. 2020. Vol. 917, no. 1. P. 012047. doi: 10.1088/1757-899X/917/1/012047.
  14. Gomez L. C., Herv as R., Gonz alez I., Villarreal V. Studying the generalisability of cognitive load measured with EEG // Biomedical Signal Processing and Control. 2021. Vol. 70. P. 103032. doi: 10.1016/j.bspc.2021.103032.
  15. Caillet B., Devenes S., Ma ` tre G., Hight D., Mirra A., Levionnois O., Simalatsar A. General Anaesthesia Matlab-based Graphical User Interface: A tool for EEG signal acquisition, processing and visualisation offline and in real-time. 2023. doi: 10.13140/RG.2.2.33243.68647.
  16. Cao J., Zhao Y., Shan X., Wei H. L., Guo Y., Chen L., Erkoyuncu J. A., Sarrigiannis P. G. Brain functional and effective connectivity based on electroencephalography recordings: A review // Hum. Brain Mapp. 2022. Vol. 43, no. 2. P. 860–879. doi: 10.1002/hbm.25683.
  17. Cabanero L., Herv as R., Gonz alez I., Fontecha J., Mond ejar T., Bravo J. Characterisation of mobile-device tasks by their associated cognitive load through EEG data processing // Future Generation Computer Systems. 2020. Vol. 113. P. 380–390. doi: 10.1016/j.future.2020.07.013.
  18. Costadopoulos N., Islam M. Z., Tien D. A knowledge discovery and visualisation method for unearthing emotional states from physiological data // Int. J. Mach. Learn. & Cyber. 2021. Vol. 12, no. 3. P. 843–858. doi: 10.1007/s13042-020-01205-4.
  19. Montazeri S., Pinchefsky E., Tse I., Marchi V., Kohonen J., Kauppila M., Airaksinen M., Tapani K., Nevalainen P., Hahn C., Tam E. W. Y., Stevenson N. J., Vanhatalo S. Building an open source classifier for the neonatal EEG background: A systematic feature-based approach from expert scoring to clinical visualization // Front. Hum. Neurosci. 2021. Vol. 15. P. 675154. doi: 10.3389/fnhum.2021.675154.
  20. Goldberger A. L., Amaral L. A., Glass L., Hausdorff J. M., Ivanov P. C., Mark R. G., Mietus J. E., Moody G. B., Peng C. K., Stanley H. E. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals // Circulation. 2000. Vol. 101, no. 23. P. E215–E220. doi: 10.1161/01.cir.101.23.e215.
  21. Zym I., Tukaev S., Seleznov I., Kiyono K., Popov A., Chernykh M., Shpenkov O. Electroencephalograms during Mental Arithmetic Task Performance // Data. 2019. Vol. 4, no. 1. P. 14. doi: 10.3390/data4010014.
  22. Stepanyan I. V., Lednev M. Y.. Visualization of the Signals Entropy Structure Based on Walsh– Hadamard Functions // Symmetry. 2024. Vol. 16, no. 1. P. 59. doi: 10.3390/sym16010059.
  23. Степанян И. В., Леднев М. Ю. Алгоритмы визуализации молекулярно-генетических последовательностей в пространствах двоично-ортогональных функций Уолша. М.: «КДУ», «Добросвет», 2020. 193 с. doi: 10.31453/kdu.ru.978-5-7913-1159-7-2020-193.
  24. Аристов В. В., Кубряк О. В., Степанян И. В. Расчёт циклических характеристик электроэнцефалограммы для исследования электрической активности мозга // Известия вузов. ПНД. 2023. Т. 31, № 4. С. 469–483. doi: 10.18500/0869-6632-003051.

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