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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Vydané v:	Frontiers in neuroinformatics Ročník 17; s. 1301718
Hlavní autori:	Mikhaylets, Ekaterina, Razorenova, Alexandra M., Chernyshev, Vsevolod, Syrov, Nikolay, Yakovlev, Lev, Boytsova, Julia, Kokurina, Elena, Zhironkina, Yulia, Medvedev, Svyatoslav, Kaplan, Alexander
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland Frontiers Research Foundation 29.01.2024 Frontiers Media S.A
Predmet:	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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