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's hie...

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Hlavní autoři:	Ekaterina Mikhaylets, Alexandra M. Razorenova, Vsevolod Chernyshev, Nikolay Syrov, Lev Yakovlev, Julia Boytsova, Elena Kokurina, Yulia Zhironkina, Svyatoslav Medvedev, Alexander Kaplan
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Frontiers Media S.A. 2024-01-01
Edice:	Frontiers in Neuroinformatics
Témata:	EEG clustering unsupervised data annotation information value meditation practice Ward's method
On-line přístup:	https://www.frontiersin.org/articles/10.3389/fninf.2023.1301718/full
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