Standard

Approximate Hindmarsh-Rose model identification: application to EEG data. / Ковальчуков, Александр Алексеевич.

2023 7th Scientific School Dynamics of Complex Networks and their Applications (DCNA). Institute of Electrical and Electronics Engineers Inc., 2023. p. 151-154.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Ковальчуков, АА 2023, Approximate Hindmarsh-Rose model identification: application to EEG data. in 2023 7th Scientific School Dynamics of Complex Networks and their Applications (DCNA). Institute of Electrical and Electronics Engineers Inc., pp. 151-154, 2023 7th Scientific School Dynamics of Complex Networks and their Applications (DCNA), Калининград, Russian Federation, 18/09/23. https://doi.org/10.1109/DCNA59899.2023

APA

Ковальчуков, А. А. (2023). Approximate Hindmarsh-Rose model identification: application to EEG data. In 2023 7th Scientific School Dynamics of Complex Networks and their Applications (DCNA) (pp. 151-154). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DCNA59899.2023

Vancouver

Ковальчуков АА. Approximate Hindmarsh-Rose model identification: application to EEG data. In 2023 7th Scientific School Dynamics of Complex Networks and their Applications (DCNA). Institute of Electrical and Electronics Engineers Inc. 2023. p. 151-154 https://doi.org/10.1109/DCNA59899.2023

Author

Ковальчуков, Александр Алексеевич. / Approximate Hindmarsh-Rose model identification: application to EEG data. 2023 7th Scientific School Dynamics of Complex Networks and their Applications (DCNA). Institute of Electrical and Electronics Engineers Inc., 2023. pp. 151-154

BibTeX

@inproceedings{2ab6ba39131d4b0dae474276136182f4,
title = "Approximate Hindmarsh-Rose model identification: application to EEG data",
abstract = "The parameters of the Hindmarsh-Rose model are evaluated by measuring the output of the model and estimating the derivatives of the output since direct derivatives measurement leads to poor conditionality of the problem. A {"}Dirty-Derivative{"} filter of the third order is used to evaluate them, which gives a smoothed estimate of derivatives. The parameter estimation algorithm is applied to real EEG data and demonstrates convergence of tuning parameters to some values. The obtained parameter estimates are used as features for epileptic seizure classification problem.",
author = "Ковальчуков, {Александр Алексеевич}",
note = "Публикация проиндексирована Scopus: https://www.scopus.com/authid/detail.uri?authorId=57491564300; null ; Conference date: 18-09-2023 Through 20-09-2023",
year = "2023",
month = oct,
day = "27",
doi = "10.1109/DCNA59899.2023",
language = "English",
pages = "151--154",
booktitle = "2023 7th Scientific School Dynamics of Complex Networks and their Applications (DCNA)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",
url = "https://ieeexplore.ieee.org/xpl/conhome/10290133/proceeding",

}

RIS

TY - GEN

T1 - Approximate Hindmarsh-Rose model identification: application to EEG data

AU - Ковальчуков, Александр Алексеевич

N1 - Публикация проиндексирована Scopus: https://www.scopus.com/authid/detail.uri?authorId=57491564300

PY - 2023/10/27

Y1 - 2023/10/27

N2 - The parameters of the Hindmarsh-Rose model are evaluated by measuring the output of the model and estimating the derivatives of the output since direct derivatives measurement leads to poor conditionality of the problem. A "Dirty-Derivative" filter of the third order is used to evaluate them, which gives a smoothed estimate of derivatives. The parameter estimation algorithm is applied to real EEG data and demonstrates convergence of tuning parameters to some values. The obtained parameter estimates are used as features for epileptic seizure classification problem.

AB - The parameters of the Hindmarsh-Rose model are evaluated by measuring the output of the model and estimating the derivatives of the output since direct derivatives measurement leads to poor conditionality of the problem. A "Dirty-Derivative" filter of the third order is used to evaluate them, which gives a smoothed estimate of derivatives. The parameter estimation algorithm is applied to real EEG data and demonstrates convergence of tuning parameters to some values. The obtained parameter estimates are used as features for epileptic seizure classification problem.

UR - https://ieeexplore.ieee.org/document/10290298

U2 - 10.1109/DCNA59899.2023

DO - 10.1109/DCNA59899.2023

M3 - Conference contribution

SP - 151

EP - 154

BT - 2023 7th Scientific School Dynamics of Complex Networks and their Applications (DCNA)

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 18 September 2023 through 20 September 2023

ER -

ID: 116450403