Literature DB >> 11467912

Topographic time-frequency decomposition of the EEG.

T Koenig1, F Marti-Lopez, P Valdes-Sosa.   

Abstract

Frequency-transformed EEG resting data has been widely used to describe normal and abnormal brain functional states as function of the spectral power in different frequency bands. This has yielded a series of clinically relevant findings. However, by transforming the EEG into the frequency domain, the initially excellent time resolution of time-domain EEG is lost. The topographic time-frequency decomposition is a novel computerized EEG analysis method that combines previously available techniques from time-domain spatial EEG analysis and time-frequency decomposition of single-channel time series. It yields a new, physiologically and statistically plausible topographic time-frequency representation of human multichannel EEG. The original EEG is accounted by the coefficients of a large set of user defined EEG like time-series, which are optimized for maximal spatial smoothness and minimal norm. These coefficients are then reduced to a small number of model scalp field configurations, which vary in intensity as a function of time and frequency. The result is thus a small number of EEG field configurations, each with a corresponding time-frequency (Wigner) plot. The method has several advantages: It does not assume that the data is composed of orthogonal elements, it does not assume stationarity, it produces topographical maps and it allows to include user-defined, specific EEG elements, such as spike and wave patterns. After a formal introduction of the method, several examples are given, which include artificial data and multichannel EEG during different physiological and pathological conditions. Copyright 2001 Academic Press.

Entities:  

Mesh:

Year:  2001        PMID: 11467912     DOI: 10.1006/nimg.2001.0825

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  11 in total

Review 1.  Brain connectivity at different time-scales measured with EEG.

Authors:  T Koenig; D Studer; D Hubl; L Melie; W K Strik
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2005-05-29       Impact factor: 6.237

2.  Intelligence and EEG current density using low-resolution electromagnetic tomography (LORETA).

Authors:  R W Thatcher; D North; C Biver
Journal:  Hum Brain Mapp       Date:  2007-02       Impact factor: 5.038

3.  EEG Microstates Predict Concurrent fMRI Dynamic Functional Connectivity States.

Authors:  Rodolfo Abreu; João Jorge; Alberto Leal; Thomas Koenig; Patrícia Figueiredo
Journal:  Brain Topogr       Date:  2020-11-07       Impact factor: 3.020

4.  Dynamical principles of emotion-cognition interaction: mathematical images of mental disorders.

Authors:  Mikhail I Rabinovich; Mehmet K Muezzinoglu; Irina Strigo; Alexander Bystritsky
Journal:  PLoS One       Date:  2010-09-21       Impact factor: 3.240

5.  IMI2-PainCare-BioPain-RCT3: a randomized, double-blind, placebo-controlled, crossover, multi-center trial in healthy subjects to investigate the effects of lacosamide, pregabalin, and tapentadol on biomarkers of pain processing observed by electroencephalography (EEG).

Authors:  Keith G Phillips; Rolf-Detlef Treede; André Mouraux; Petra Bloms-Funke; Irmgard Boesl; Ombretta Caspani; Sonya C Chapman; Giulia Di Stefano; Nanna Brix Finnerup; Luis Garcia-Larrea; Marcus Goetz; Anna Kostenko; Bernhard Pelz; Esther Pogatzki-Zahn; Karin Schubart; Alexandre Stouffs; Andrea Truini; Irene Tracey; Iñaki F Troconiz; Johannes Van Niel; Jose Miguel Vela; Katy Vincent; Jan Vollert; Vishvarani Wanigasekera; Matthias Wittayer
Journal:  Trials       Date:  2021-06-17       Impact factor: 2.279

6.  Dynamic coherence analysis of resting fMRI data to jointly capture state-based phase, frequency, and time-domain information.

Authors:  Maziar Yaesoubi; Elena A Allen; Robyn L Miller; Vince D Calhoun
Journal:  Neuroimage       Date:  2015-07-08       Impact factor: 6.556

7.  Spatiotemporal analysis of multichannel EEG: CARTOOL.

Authors:  Denis Brunet; Micah M Murray; Christoph M Michel
Journal:  Comput Intell Neurosci       Date:  2011-01-05

Review 8.  EEG-Informed fMRI: A Review of Data Analysis Methods.

Authors:  Rodolfo Abreu; Alberto Leal; Patrícia Figueiredo
Journal:  Front Hum Neurosci       Date:  2018-02-06       Impact factor: 3.169

9.  Developmental changes of BOLD signal correlations with global human EEG power and synchronization during working memory.

Authors:  Lars Michels; Rafael Lüchinger; Thomas Koenig; Ernst Martin; Daniel Brandeis
Journal:  PLoS One       Date:  2012-07-06       Impact factor: 3.240

10.  Assessment of Multivariate Neural Time Series by Phase Synchrony Clustering in a Time-Frequency-Topography Representation.

Authors:  M A Porta-Garcia; R Valdes-Cristerna; O Yanez-Suarez
Journal:  Comput Intell Neurosci       Date:  2018-03-21
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