TY - JOUR

T1 - Adaptive Parameter Identification for a Class of Neural Mass Models with Application to Ergatic Systems

AU - Plotnikov, S.A.

AU - Fradkov, A.L.

N1 - Export Date: 4 March 2024 Адрес для корреспонденции: Plotnikov, S.A.; Institute for Problems in Mechanical Engineering, Russian Federation; эл. почта: waterwalf@gmail.com Сведения о финансировании: Ministry of Education and Science of the Russian Federation, Minobrnauka, 075-15-2021-573 Сведения о финансировании: Saint Petersburg State University, SPbU, 94034465 Текст о финансировании 1: Simulation was performed in IPME RAS and supported by the Ministry of Science and Higher Education of the Russian Federation (Project No. 075-15-2021-573) For citation: Plotnikov S. A., Fradkov A. L. Adaptive Parameter Identification for a Class of Neural Mass Models with Application to Ergatic Systems, Mekhatronika, Avtomatizatsiya, Upravlenie, 2024, vol. 25, no. 1, pp. 13—18. Текст о финансировании 2: Keywords: ergatic systems, adaptive parameter identifier, neural mass model, Lyapunov function method Acknowledgements: Adaptive parameter identifier synthesis was supported by Saint Petersburg State University, project ID: 94034465. Пристатейные ссылки: Sergeev, S. F., Biomorphic neuroadaptive interfaces in the ergatic systems: problems and solutions (2016) Mekhatronika, Avtomatizatsiya, Upravlenie, 17 (9), pp. 599-605. , Сергеев С. Ф. Нейроадаптивные биоморфные интерфейсы в эргатических системах: проблемы и решения Мехатроника, автоматизация, управление. 2016. Т. 17, 9. С. 599—605. 1. (In Russian); Guselnikov, V. I., (1976) Electrophysiology of the brain, p. 423. , Гусельников В. И. Электрофизиология головного мозга. М.: Высшая школа, 1976. 423 с. 2. Moscow, High school, (in Russian); Kropotov, J. D., (2009) Quantitative EEG, event-related potentials and neurotherapy, , Кропотов Ю. Д. Количественная ЭЭГ, когнитивные вызванные потенциалы мозга человека и нейротерапия. Донецк: Издатель Заславский А. Ю., 2010. 512 с. 3. London, Elsevier, 533; Sharmila, A., Epilepsy detection from EEG signals: a review (2018) J. Med. Eng. Technol, 42 (5), pp. 368-380. , N; Silva, G., Alves, M., Cunha, R., Bispo, B. C., Rodrigues, P. M., Parkinson disease early detection using EEG channels cross-correlation (2020) Int. J. Appl. Eng. Res, 15 (3), pp. 197-203. , N; Schielke, A., Krekelberg, B., Steady state visual evoked potentials in schizophrenia: a review (2022) Front. Neurosci, 16; Meachon, E. J., Meyer, M., Wilmut, K., Zemp, M., Alpers, G. W., Evoked potentials differentiate developmental coordination disorder from attention-deficit/hyperactivity disorder in a stop-signal task: a pilot study (2021) Front. Hum. Neurosci, 15; Jansen, B. H., Rit, V. G., Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns (1995) Biol. Cybern, 73, pp. 357-366; Postoyan, R., Chong, M., Nesic, D., Kuhlmann, L., Parameter and state estimation for a class of neural mass models (2012) 51st IEEE Conference on Decision and Control (CDC), pp. 2322-2327; Liu, X., Sun, C.-X., Gao, Q., Chen, Z.-W., A passivity-based observer for neural mass models (2019) IMA J. Math. Control. Inf, 36 (3), pp. 701-711. , N; Sun, C.-X., Liu, X., A state observer for the computational network model of neural populations (2021) Chaos, 31, p. 013127; Hodgkin, A. L., Huxley, A. F., A quantitative description of membrane current and its application to conduction and excitation in nerve (1952) J. Physiol, 117 (4), pp. 500-544. , N; Hindmarsh, J. L., Rose, R. M., A model of neuronal bursting using three coupled first order differential equations (1984) Proc. R. Soc. Lond. B Biol. Sci, 221 (1222), pp. 87-102. , N; FitzHugh, R., Impulses and physiological states in theoretical models of nerve membrane (1961) Biophys. J, 1 (6), pp. 445-466. , N; Nagumo, J., Arimoto, S, Yoshizawa, S., An active pulse transmission line simulating nerve axon (1962) Proc. IRE, 50 (10), pp. 2061-2070. , N; Lapicque, L., Recherches quantitatives sur l’excitation électrique des nerfs traitée comme une polarization (1907) J. Physiol. Pathol. Gen, 9, pp. 620-635; McCulloch, W., Pitts, W., A logical calculus of the ideas immanent in nervous activity (1943) Bull. Math. Biophys, 5, pp. 115-133; van, Rotterdam A., Lopes da Silva, F. H., van der, Ende J., Viergever, M. A., Hermans, A. J., A model of the spatial-temporal characteristics of the alpha rhythm (1982) Bull. Math. Biol, 44, pp. 283-305; Dodt, H. U., Pawelzik, H., Zieglgansberger, W., Actions of noradrenaline on neocortical neurons in vitro (1991) Brain Res, 545, pp. 307-311; Jansen, B. H., Zouridakis, G., Brandt, M. E., A neuro-physiologically based mathematical model of flash visual evoked potentials (1993) Biol. Cybern, 68, pp. 275-283

PY - 2024/1/10

Y1 - 2024/1/10

N2 - This paper considers one of the problems that arise in the developing of the ergatic brain-computer interfaces. This technology allows a person to control various mechatronic systems through the "power of thought", i.e. based on the registration of electrical activity of the brain. The problem is the complexity and poor knowledge of the brain. To describe the electrical activity of the brain, various models of neural ensembles are used, one of which is the neural mass model proposed by Jansen and Rit in 1995. To tune the parameters of this model according to real data, it is proposed to use an adaptive parameter identifier. An important condition for the synthesis of an adaptive identifier is that only the system output, which is the potential difference between two points of the head, can be measured. At the beginning, it is assumed that the entire state vector of the neural mass model is available for measurement. An identifier is synthesized to tune the parameters of such a system and its convergence is proved using the Lyapunov function method. Further, the obtained identifier is refined in such a way that it uses only the output of the system. To do this, using the finite difference method, the output derivative of the neural mass model is approximately calculated, which is used to make several replacements of the unknown components of the state vector. It is very difficult to analytically prove the convergence of the obtained adaptive parameter identifier, therefore, the possibility of using it to estimate the parameters of a neural mass model is checked using simulation. The synthesized identifier uses only the system output to tune the parameters, which in the future will allow us to consider real data instead of the system output. Thus, this identifier can be used to tune the parameters of the neural mass model based on real data. © 2024 New Technologies Publishing House. All rights reserved.

AB - This paper considers one of the problems that arise in the developing of the ergatic brain-computer interfaces. This technology allows a person to control various mechatronic systems through the "power of thought", i.e. based on the registration of electrical activity of the brain. The problem is the complexity and poor knowledge of the brain. To describe the electrical activity of the brain, various models of neural ensembles are used, one of which is the neural mass model proposed by Jansen and Rit in 1995. To tune the parameters of this model according to real data, it is proposed to use an adaptive parameter identifier. An important condition for the synthesis of an adaptive identifier is that only the system output, which is the potential difference between two points of the head, can be measured. At the beginning, it is assumed that the entire state vector of the neural mass model is available for measurement. An identifier is synthesized to tune the parameters of such a system and its convergence is proved using the Lyapunov function method. Further, the obtained identifier is refined in such a way that it uses only the output of the system. To do this, using the finite difference method, the output derivative of the neural mass model is approximately calculated, which is used to make several replacements of the unknown components of the state vector. It is very difficult to analytically prove the convergence of the obtained adaptive parameter identifier, therefore, the possibility of using it to estimate the parameters of a neural mass model is checked using simulation. The synthesized identifier uses only the system output to tune the parameters, which in the future will allow us to consider real data instead of the system output. Thus, this identifier can be used to tune the parameters of the neural mass model based on real data. © 2024 New Technologies Publishing House. All rights reserved.

KW - adaptive parameter identifier

KW - ergatic systems

KW - Lyapunov function method

KW - neural mass model

UR - https://www.mendeley.com/catalogue/e1e55add-9267-35a0-885f-a6250714eb47/

U2 - 10.17587/mau.25.13-18

DO - 10.17587/mau.25.13-18

M3 - статья

VL - 25

SP - 13

EP - 18

JO - Mekhatronika, Avtomatizatsiya, Upravlenie

JF - Mekhatronika, Avtomatizatsiya, Upravlenie

SN - 1684-6427

IS - 1

ER -