A Tensor Decomposition-Based Approach for Detecting Dynamic Network States From EEG

• Published in: IEEE transactions on biomedical engineering Vol. 64; no. 1; pp. 225 - 237
• Main Authors: Mahyari, Arash Golibagh, Zoltowski, David M., Bernat, Edward M., Aviyente, Selin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Brain; Brain Mapping - methods; Cerebral Cortex - physiology; Cognitive ability; Complex networks; Computer Simulation; Control tasks; Decomposition; Diagnosis, Computer-Assisted - methods; Dissolution; dynamic functional connectivity (dFC); EEG; Electroencephalography; Electroencephalography - methods; Hidden Markov models; Humans; Mathematical analysis; Matrix decomposition; Models, Neurological; Nerve Net - physiology; Networks; Neural networks; phase synchrony; Principal component analysis; Reproducibility of Results; Sensitivity and Specificity; Singular value decomposition; Tensile stress; tensor decomposition; Tensors; Time-frequency analysis

Functional connectivity (FC), defined as the statistical dependency between distinct brain regions, has been an important tool in understanding cognitive brain processes. Most of the current works in FC have focused on the assumption of temporally stationary networks. However, recent empirical work...