SDA: a data-driven algorithm that detects functional states applied to the EEG of Guhyasamaja meditation

The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates on unlabeled data and detects optimal change-points among intrinsic functional states in time-series data based on an ensemble of Ward'...

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Veröffentlicht in:	Frontiers in neuroinformatics Jg. 17; S. 1301718
Hauptverfasser:	Mikhaylets, Ekaterina, Razorenova, Alexandra M., Chernyshev, Vsevolod, Syrov, Nikolay, Yakovlev, Lev, Boytsova, Julia, Kokurina, Elena, Zhironkina, Yulia, Medvedev, Svyatoslav, Kaplan, Alexander
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland Frontiers Research Foundation 29.01.2024 Frontiers Media S.A
Schlagworte:	Algorithms Buddhism Clustering Cognitive ability Datasets EEG Experiments information value Mathematical models Meditation meditation practice Monks Neural networks Neuroscience Questionnaires Statistical analysis Time series Trends unsupervised data annotation Ward's method unsupervised data annotation change point detection functional states meditation practice EEG Ward's method information value clustering
ISSN:	1662-5196, 1662-5196
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Abstract	The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates on unlabeled data and detects optimal change-points among intrinsic functional states in time-series data based on an ensemble of Ward's hierarchical clustering with time-connectivity constraint. The algorithm chooses the best number of states and optimal state boundaries, maximizing clustering quality metrics. We also introduce a series of methods to estimate the performance and confidence of the SDA when the ground truth annotation is unavailable. These include information value analysis, paired statistical tests, and predictive modeling analysis. The SDA was validated on EEG recordings of Guhyasamaja meditation practice with a strict staged protocol performed by three experienced Buddhist practitioners in an ecological setup. The SDA used neurophysiological descriptors as inputs, including PSD, power indices, coherence, and PLV. Post-hoc analysis of the obtained EEG states revealed significant differences compared to the baseline and neighboring states. The SDA was found to be stable with respect to state order organization and showed poor clustering quality metrics and no statistical significance between states when applied to randomly shuffled epochs (i.e., surrogate subject data used as controls). The SDA can be considered a general data-driven approach that detects hidden functional states associated with the mental processes evolving during meditation or other ongoing mental and cognitive processes.
AbstractList	The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates on unlabeled data and detects optimal change-points among intrinsic functional states in time-series data based on an ensemble of Ward's hierarchical clustering with time-connectivity constraint. The algorithm chooses the best number of states and optimal state boundaries, maximizing clustering quality metrics. We also introduce a series of methods to estimate the performance and confidence of the SDA when the ground truth annotation is unavailable. These include information value analysis, paired statistical tests, and predictive modeling analysis. The SDA was validated on EEG recordings of Guhyasamaja meditation practice with a strict staged protocol performed by three experienced Buddhist practitioners in an ecological setup. The SDA used neurophysiological descriptors as inputs, including PSD, power indices, coherence, and PLV. Post-hoc analysis of the obtained EEG states revealed significant differences compared to the baseline and neighboring states. The SDA was found to be stable with respect to state order organization and showed poor clustering quality metrics and no statistical significance between states when applied to randomly shuffled epochs (i.e., surrogate subject data used as controls). The SDA can be considered a general data-driven approach that detects hidden functional states associated with the mental processes evolving during meditation or other ongoing mental and cognitive processes. The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates on unlabeled data and detects optimal change-points among intrinsic functional states in time-series data based on an ensemble of Ward's hierarchical clustering with time-connectivity constraint. The algorithm chooses the best number of states and optimal state boundaries, maximizing clustering quality metrics. We also introduce a series of methods to estimate the performance and confidence of the SDA when the ground truth annotation is unavailable. These include information value analysis, paired statistical tests, and predictive modeling analysis. The SDA was validated on EEG recordings of Guhyasamaja meditation practice with a strict staged protocol performed by three experienced Buddhist practitioners in an ecological setup. The SDA used neurophysiological descriptors as inputs, including PSD, power indices, coherence, and PLV. Post-hoc analysis of the obtained EEG states revealed significant differences compared to the baseline and neighboring states. The SDA was found to be stable with respect to state order organization and showed poor clustering quality metrics and no statistical significance between states when applied to randomly shuffled epochs (i.e., surrogate subject data used as controls). The SDA can be considered a general data-driven approach that detects hidden functional states associated with the mental processes evolving during meditation or other ongoing mental and cognitive processes.The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates on unlabeled data and detects optimal change-points among intrinsic functional states in time-series data based on an ensemble of Ward's hierarchical clustering with time-connectivity constraint. The algorithm chooses the best number of states and optimal state boundaries, maximizing clustering quality metrics. We also introduce a series of methods to estimate the performance and confidence of the SDA when the ground truth annotation is unavailable. These include information value analysis, paired statistical tests, and predictive modeling analysis. The SDA was validated on EEG recordings of Guhyasamaja meditation practice with a strict staged protocol performed by three experienced Buddhist practitioners in an ecological setup. The SDA used neurophysiological descriptors as inputs, including PSD, power indices, coherence, and PLV. Post-hoc analysis of the obtained EEG states revealed significant differences compared to the baseline and neighboring states. The SDA was found to be stable with respect to state order organization and showed poor clustering quality metrics and no statistical significance between states when applied to randomly shuffled epochs (i.e., surrogate subject data used as controls). The SDA can be considered a general data-driven approach that detects hidden functional states associated with the mental processes evolving during meditation or other ongoing mental and cognitive processes. The paper presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates on unlabelled data and detects optimal change-points between intrinsic functional states in time-series data based on an ensemble of Ward's hierarchical clustering with time-connectivity constraint. The algorithm chooses the best number of states and optimal state boundaries, maximizing clustering quality metrics. We also introduce a series of methods to estimate the performance and confidence of the SDA when the ground truth annotation is unavailable. These include Information Value analysis, paired statistical tests, and predictive modelling analysis. The SDA was validated on EEG recordings of Guhyasamaja meditation practice with a strict staged protocol performed by three experienced Buddhist practitioners in an ecological setup. The SDA used neurophysiological descriptors as inputs, including PSD, power indices, coherence, and PLV. Post-hoc analysis of the obtained EEG states revealed significant differences compared to the baseline and neighboring states. The SDA was found to be stable with respect to state order organization and showed poor clustering quality metrics and no statistical significance between states when applied to randomly shuffled epochs (i.e., surrogate subject data used as controls). The SDA can be considered a general data-driven approach that detects hidden functional states associated with the mental processes evolving during meditation or other ongoing mental and cognitive processes. The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates on unlabeled data and detects optimal change-points among intrinsic functional states in time-series data based on an ensemble of Ward's hierarchical clustering with time-connectivity constraint. The algorithm chooses the best number of states and optimal state boundaries, maximizing clustering quality metrics. We also introduce a series of methods to estimate the performance and confidence of the SDA when the ground truth annotation is unavailable. These include information value analysis, paired statistical tests, and predictive modeling analysis. The SDA was validated on EEG recordings of Guhyasamaja meditation practice with a strict staged protocol performed by three experienced Buddhist practitioners in an ecological setup. The SDA used neurophysiological descriptors as inputs, including PSD, power indices, coherence, and PLV. Post-hoc analysis of the obtained EEG states revealed significant differences compared to the baseline and neighboring states. The SDA was found to be stable with respect to state order organization and showed poor clustering quality metrics and no statistical significance between states when applied to randomly shuffled epochs (i.e., surrogate subject data used as controls). The SDA can be considered a general data-driven approach that detects hidden functional states associated with the mental processes evolving during meditation or other ongoing mental and cognitive processes. The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates on unlabeled data and detects optimal change-points among intrinsic functional states in time-series data based on an ensemble of Ward's hierarchical clustering with time-connectivity constraint. The algorithm chooses the best number of states and optimal state boundaries, maximizing clustering quality metrics. We also introduce a series of methods to estimate the performance and confidence of the SDA when the ground truth annotation is unavailable. These include information value analysis, paired statistical tests, and predictive modeling analysis. The SDA was validated on EEG recordings of Guhyasamaja meditation practice with a strict staged protocol performed by three experienced Buddhist practitioners in an ecological setup. The SDA used neurophysiological descriptors as inputs, including PSD, power indices, coherence, and PLV. analysis of the obtained EEG states revealed significant differences compared to the baseline and neighboring states. The SDA was found to be stable with respect to state order organization and showed poor clustering quality metrics and no statistical significance between states when applied to randomly shuffled epochs (i.e., surrogate subject data used as controls). The SDA can be considered a general data-driven approach that detects hidden functional states associated with the mental processes evolving during meditation or other ongoing mental and cognitive processes.
Author	Mikhaylets, Ekaterina Razorenova, Alexandra M. Yakovlev, Lev Kokurina, Elena Zhironkina, Yulia Chernyshev, Vsevolod Syrov, Nikolay Boytsova, Julia Kaplan, Alexander Medvedev, Svyatoslav
AuthorAffiliation	2 Center for Neurocognitive Research (MEG Center), Moscow State University of Psychology and Education , Moscow , Russia 5 Save Tibet Foundation , Moscow , Russia 4 Academician Natalya Bekhtereva Foundation , St. Petersburg , Russia 1 Faculty of Computer Science, Faculty of Economic Sciences, HSE University , Moscow , Russia 6 Laboratory for Neurophysiology and Neuro-Computer Interfaces, Lomonosov Moscow State University , Moscow , Russia 3 Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University , Kaliningrad , Russia
AuthorAffiliation_xml	– name: 3 Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University , Kaliningrad , Russia – name: 2 Center for Neurocognitive Research (MEG Center), Moscow State University of Psychology and Education , Moscow , Russia – name: 5 Save Tibet Foundation , Moscow , Russia – name: 4 Academician Natalya Bekhtereva Foundation , St. Petersburg , Russia – name: 6 Laboratory for Neurophysiology and Neuro-Computer Interfaces, Lomonosov Moscow State University , Moscow , Russia – name: 1 Faculty of Computer Science, Faculty of Economic Sciences, HSE University , Moscow , Russia
Author_xml	– sequence: 1 givenname: Ekaterina surname: Mikhaylets fullname: Mikhaylets, Ekaterina – sequence: 2 givenname: Alexandra M. surname: Razorenova fullname: Razorenova, Alexandra M. – sequence: 3 givenname: Vsevolod surname: Chernyshev fullname: Chernyshev, Vsevolod – sequence: 4 givenname: Nikolay surname: Syrov fullname: Syrov, Nikolay – sequence: 5 givenname: Lev surname: Yakovlev fullname: Yakovlev, Lev – sequence: 6 givenname: Julia surname: Boytsova fullname: Boytsova, Julia – sequence: 7 givenname: Elena surname: Kokurina fullname: Kokurina, Elena – sequence: 8 givenname: Yulia surname: Zhironkina fullname: Zhironkina, Yulia – sequence: 9 givenname: Svyatoslav surname: Medvedev fullname: Medvedev, Svyatoslav – sequence: 10 givenname: Alexander surname: Kaplan fullname: Kaplan, Alexander
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Copyright	Copyright © 2024 Mikhaylets, Razorenova, Chernyshev, Syrov, Yakovlev, Boytsova, Kokurina, Zhironkina, Medvedev and Kaplan. 2024. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2024 Mikhaylets, Razorenova, Chernyshev, Syrov, Yakovlev, Boytsova, Kokurina, Zhironkina, Medvedev and Kaplan. 2024 Mikhaylets, Razorenova, Chernyshev, Syrov, Yakovlev, Boytsova, Kokurina, Zhironkina, Medvedev and Kaplan
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Keywords	unsupervised data annotation change point detection functional states meditation practice EEG Ward's method information value clustering
Language	English
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Chuanliang Han, Shenzhen Institute of Advanced Technology (CAS), China Edited by: Pawel Oswiecimka, Polish Academy of Sciences, Poland Reviewed by: Arun Sasidharan, National Institute of Mental Health and Neurosciences, India
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Snippet	The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates... The paper presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates...
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SubjectTerms	Algorithms Buddhism Clustering Cognitive ability Datasets EEG Experiments information value Mathematical models Meditation meditation practice Monks Neural networks Neuroscience Questionnaires Statistical analysis Time series Trends unsupervised data annotation Ward's method
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Title	SDA: a data-driven algorithm that detects functional states applied to the EEG of Guhyasamaja meditation
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