Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
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. и др.
International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM. IEEE Computer Society, 2024. стр. 2170-2173 (International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
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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