A4 Article in conference proceedings
Identifying Task-Based Dynamic Functional Connectivity Using Tensor Decomposition (2020)

Liu, Wenya; Wang, Xiulin; Ristaniemi, Tapani; Cong, Fengyu (2020). Identifying Task-Based Dynamic Functional Connectivity Using Tensor Decomposition. In Yang, Haiqin; Pasupa, Kitsuchart; Chi-Sing Leung, Andrew; Kwok, James T.; Chan, Jonathan H.; King, Irwin (Eds.) ICONIP 2020 : 27th International Conference on Neural Information Processing, Proceedings, Part V, Communications in Computer and Information Science, 1333. Cham: Springer, 361-369. DOI: 10.1007/978-3-030-63823-8_42

JYU authors or editors

Publication details

All authors or editors: Liu, Wenya; Wang, Xiulin; Ristaniemi, Tapani; Cong, Fengyu

Parent publication: ICONIP 2020 : 27th International Conference on Neural Information Processing, Proceedings, Part V

Parent publication editors: Yang, Haiqin; Pasupa, Kitsuchart; Chi-Sing Leung, Andrew; Kwok, James T.; Chan, Jonathan H.; King, Irwin

Place and date of conference: Bangkok, Thailand, 18.-22.11.2020

ISBN: 978-3-030-63822-1

eISBN: 978-3-030-63823-8

Journal or series: Communications in Computer and Information Science

ISSN: 1865-0929

eISSN: 1865-0937

Publication year: 2020

Number in series: 1333

Pages range: 361-369

Number of pages in the book: 844

Publisher: Springer

Place of Publication: Cham

Publication country: Switzerland

Publication language: English

DOI: https://doi.org/10.1007/978-3-030-63823-8_42

Open Access: Publication channel is not openly available

Abstract

Functional connectivity (FC) patterns in human brain are dynamic in a task-specific condition, and identifying the dynamic changes is important to reveal the information processing processes and network reconfiguration in cognitive tasks. In this study, we proposed a comprehensive framework based on high-order singular value decomposition (HOSVD) to detect the stable change points of FC using electroencephalogram (EEG). First, phase lag index (PLI) method was applied to calculate FC for each time point, constructing a 3-way tensor, i.e., connectivity × connectivity × time. Then a stepwise HOSVD (SHOSVD) algorithm was proposed to detect the change points of FC, and the stability of change points were analyzed considering the different dissimilarity between different FC patterns. The transmission of seven FC patterns were identified in a task condition. We applied our methods to EEG data, and the results verified by prior knowledge demonstrated that our proposed algorithm can reliably capture the dynamic changes of FC.

Keywords: EEG; signal analysis; signal processing

Free keywords: dynamic functional connectivity; HOSVD; EEG; tensor decomposition

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Ministry reporting: Yes

Preliminary JUFO rating: 1