Surrogate Data Method Requires End-Matched Segmentation of Electroencephalographic Signals to Estimate Non-linearity
Päeske, L.; Bachmann, M.; Põld, T.; de Oliveira, S. P. M.; Lass, J.; Raik, J.; Hinrikus, H. (2018). Surrogate Data Method Requires End-Matched Segmentation of Electroencephalographic Signals to Estimate Non-linearity. Frontiers in Physiology, 9, ARTN 1350. DOI: 10.3389/fphys.2018.01350.
article in a journal
Päeske, L.; Bachmann, M.; Põld, T.; de Oliveira, S. P. M.; Lass, J.; Raik, J.; Hinrikus, H.
- English
Frontiers in Physiology
LAUSANNE
1664-042X
9
9
2018
ARTN 1350
9
Published
1.1. Scholarly articles indexed by Web of Science Science Citation Index Expanded, Social Sciences Citation Index, Arts & Humanities Citation Index, Emerging Sources Citation Index and/or indexed by Scopus (excluding chapters in books)
Yes
gold
Attribution (CC BY)
WOS
Reference to the full text
Institutions (with ETIS account)
Additional information
Article
EEG; dominant frequency; alpha frequency; surrogate data; Fourier transform; segment length
- TAR16013 (EXCITE) (TK148) "Estonian Centre of Excellence in ICT Research" (1.09.2016−1.03.2023); Principal Investigator: Maarja Kruusmaa; Tallinn University of Technology , School of Information Technologies, Centre for Biorobotics, Cybernetica AS (partner); Financier: Archimedes Foundation; Financing: 2 121 015 EUR.
- IUT19-2 "Biooptical and bioelectrical signals in Biomedical Engineering" (1.01.2014−31.12.2019); Principal Investigator: Ivo Fridolin; Tallinn University of Technology , Technomedicum of TUT, Department of Biomedical Engineering, Tallinn University of Technology , School of Information Technologies, Department of Health Technologies; Financier: Estonian Research Council; Financing: 528 000 EUR.
https://www.frontiersin.org/articles/10.3389/fphys.2018.01350/pdf