Standard

The Application of Machine-Learning Approach for the Classification of People According to Their Participation in Meditation based on Neurophysiological Data. / Istomina, Nadezhda A.; Fu, Xi; Tamozhnikov, Sergey S. et al.

International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM. IEEE Computer Society, 2024. p. 2170-2173 (International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM).

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

Harvard

Istomina, NA, Fu, X, Tamozhnikov, SS, Saprygin, AE & Savostyanov, AN 2024, The Application of Machine-Learning Approach for the Classification of People According to Their Participation in Meditation based on Neurophysiological Data. in International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM. International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM, IEEE Computer Society, pp. 2170-2173, 25th IEEE International Conference of Young Professionals in Electron Devices and Materials, Russian Federation, 28.06.2024. https://doi.org/10.1109/EDM61683.2024.10615180

APA

Istomina, N. A., Fu, X., Tamozhnikov, S. S., Saprygin, A. E., & Savostyanov, A. N. (2024). The Application of Machine-Learning Approach for the Classification of People According to Their Participation in Meditation based on Neurophysiological Data. In International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM (pp. 2170-2173). (International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM). IEEE Computer Society. https://doi.org/10.1109/EDM61683.2024.10615180

Vancouver

Istomina NA, Fu X, Tamozhnikov SS, Saprygin AE, Savostyanov AN. The Application of Machine-Learning Approach for the Classification of People According to Their Participation in Meditation based on Neurophysiological Data. In International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM. IEEE Computer Society. 2024. p. 2170-2173. (International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM). doi: 10.1109/EDM61683.2024.10615180

Author

Istomina, Nadezhda A. ; Fu, Xi ; Tamozhnikov, Sergey S. et al. / The Application of Machine-Learning Approach for the Classification of People According to Their Participation in Meditation based on Neurophysiological Data. International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM. IEEE Computer Society, 2024. pp. 2170-2173 (International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM).

BibTeX

@inproceedings{3e365fbac1cf44228f5a441c2f8b86de,
title = "The Application of Machine-Learning Approach for the Classification of People According to Their Participation in Meditation based on Neurophysiological Data",
abstract = "Motor control is a human ability to manage own goal-directed motions. This ability can be estimated by means of so-called stop-signal paradigm (SSP). SSP is an experimental approach consisting of two behavioral tasks – activation or inhibition of motions. SSP is used for diagnosing a wide range of neuropsychiatric pathologies, including depressive and anxiety disorders. Combined with the analysis of evoked brain potentials (ERP), processing of the behavioral SSP results allows to reveal the neurophysiological causes of normal motor control and its deviations. Meditation is a psychological practice aimed at reducing stress and anxiety levels. The result of long-term meditation is the increase of individuals' resilience to affective disorders. In this study, we applied a machine-learning approach to develop a methodology of classification of people according to their participation in meditation. The amplitudes of two ERP peaks (premotor and postmotor) were used as input data. We developed four convolutional network models that were trained and tested on approximately 100 healthy participants (half of whom participated in meditation). Then, all models were checked on an additional sample of 25 participants. We selected parameters for convolutional networks that allowed us to achieve 82% classification accuracy and model stability against overfitting. The proposed approach allows to classify individuals based on their stress resilience through ERP data processing.",
keywords = "convolutional neural networks, event-related brain potentials (ERPs), meditation, motor control, stop-signal paradigm",
author = "Istomina, {Nadezhda A.} and Xi Fu and Tamozhnikov, {Sergey S.} and Saprygin, {Alexander E.} and Savostyanov, {Alexander N.}",
note = "The study was carried out with the support of the grant of the Russian Research Foundation No. 22-15-00142 \{"}fMRI and EEG correlates the focus of attention on the own person as a factor redisposition to affective disorders.\{"} The collection and analysis of genetic data was performed within the budget project of the ICG SB RAS No. FWNR-2022-0020.; 25th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2024 ; Conference date: 28-06-2024 Through 02-07-2024",
year = "2024",
doi = "10.1109/EDM61683.2024.10615180",
language = "English",
isbn = "9798350389234",
series = "International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM",
publisher = "IEEE Computer Society",
pages = "2170--2173",
booktitle = "International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM",
address = "United States",
url = "https://edm.ieeesiberia.org/",

}

RIS

TY - GEN

T1 - The Application of Machine-Learning Approach for the Classification of People According to Their Participation in Meditation based on Neurophysiological Data

AU - Istomina, Nadezhda A.

AU - Fu, Xi

AU - Tamozhnikov, Sergey S.

AU - Saprygin, Alexander E.

AU - Savostyanov, Alexander N.

N1 - Conference code: 25

PY - 2024

Y1 - 2024

N2 - Motor control is a human ability to manage own goal-directed motions. This ability can be estimated by means of so-called stop-signal paradigm (SSP). SSP is an experimental approach consisting of two behavioral tasks – activation or inhibition of motions. SSP is used for diagnosing a wide range of neuropsychiatric pathologies, including depressive and anxiety disorders. Combined with the analysis of evoked brain potentials (ERP), processing of the behavioral SSP results allows to reveal the neurophysiological causes of normal motor control and its deviations. Meditation is a psychological practice aimed at reducing stress and anxiety levels. The result of long-term meditation is the increase of individuals' resilience to affective disorders. In this study, we applied a machine-learning approach to develop a methodology of classification of people according to their participation in meditation. The amplitudes of two ERP peaks (premotor and postmotor) were used as input data. We developed four convolutional network models that were trained and tested on approximately 100 healthy participants (half of whom participated in meditation). Then, all models were checked on an additional sample of 25 participants. We selected parameters for convolutional networks that allowed us to achieve 82% classification accuracy and model stability against overfitting. The proposed approach allows to classify individuals based on their stress resilience through ERP data processing.

AB - Motor control is a human ability to manage own goal-directed motions. This ability can be estimated by means of so-called stop-signal paradigm (SSP). SSP is an experimental approach consisting of two behavioral tasks – activation or inhibition of motions. SSP is used for diagnosing a wide range of neuropsychiatric pathologies, including depressive and anxiety disorders. Combined with the analysis of evoked brain potentials (ERP), processing of the behavioral SSP results allows to reveal the neurophysiological causes of normal motor control and its deviations. Meditation is a psychological practice aimed at reducing stress and anxiety levels. The result of long-term meditation is the increase of individuals' resilience to affective disorders. In this study, we applied a machine-learning approach to develop a methodology of classification of people according to their participation in meditation. The amplitudes of two ERP peaks (premotor and postmotor) were used as input data. We developed four convolutional network models that were trained and tested on approximately 100 healthy participants (half of whom participated in meditation). Then, all models were checked on an additional sample of 25 participants. We selected parameters for convolutional networks that allowed us to achieve 82% classification accuracy and model stability against overfitting. The proposed approach allows to classify individuals based on their stress resilience through ERP data processing.

KW - convolutional neural networks

KW - event-related brain potentials (ERPs)

KW - meditation

KW - motor control

KW - stop-signal paradigm

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85201966112&origin=inward&txGid=9b16c4184d839b9c120d8618ba074f0a

UR - https://www.mendeley.com/catalogue/9c100cf7-79c7-37a7-b36f-b13edd769467/

U2 - 10.1109/EDM61683.2024.10615180

DO - 10.1109/EDM61683.2024.10615180

M3 - Conference contribution

SN - 9798350389234

T3 - International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM

SP - 2170

EP - 2173

BT - International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM

PB - IEEE Computer Society

T2 - 25th IEEE International Conference of Young Professionals in Electron Devices and Materials

Y2 - 28 June 2024 through 2 July 2024

ER -

ID: 60548792