SDA: a data-driven algorithm that detects functional states applied to the EEG of Guhyasamaja meditation
Frontiers in Neuroinformatics January 29, 2024 Ekaterina Mikhaylets, Alexandra Razorenova, П. Н. Николаев et al. 2 citations
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...