A better oscillation detection method robustly extracts EEG rhythms across brain state changes: The human alpha rhythm as a test case

NeuroImage ◽  
2011 ◽  
Vol 54 (2) ◽  
pp. 860-874 ◽  
Author(s):  
Tara A. Whitten ◽  
Adam M. Hughes ◽  
Clayton T. Dickson ◽  
Jeremy B. Caplan
Keyword(s):  
Alpha Rhythm ◽  
Detection Method ◽  
Brain State ◽  
Test Case ◽  
Eeg Rhythms ◽  
State Changes ◽  
Oscillation Detection

Detecting Brain State Changes via Manifold Mean Shifting

2021 ◽  
Author(s):  
Zhuobin Huang ◽  
Tingting Dan ◽  
Yi Lin ◽  
Jiazhou Chen ◽  
Hongmin Cai ◽  
...  
Keyword(s):  
Brain State ◽  
State Changes

Bifurcations and state changes in the human alpha rhythm: Theory and experiment

2009 ◽  
pp. 117-145 ◽  
Author(s):  
D.T.J. Liley ◽  
I. Bojak ◽  
M.P. Dafilis ◽  
L. van Veen ◽  
F. Frascoli ◽  
...  
Keyword(s):  
Alpha Rhythm ◽  
State Changes ◽  
Theory And Experiment

Detecting Brain State Changes by Geometric Deep Learning of Functional Dynamics on Riemannian Manifold

2021 ◽  
pp. 543-552
Author(s):  
Zhuobin Huang ◽  
Hongmin Cai ◽  
Tingting Dan ◽  
Yi Lin ◽  
Paul Laurienti ◽  
...  
Keyword(s):  
Deep Learning ◽  
Riemannian Manifold ◽  
Brain State ◽  
Functional Dynamics ◽  
State Changes

The DCT-based oscillation detection method for a single time series

2010 ◽  
Vol 20 (5) ◽  
pp. 609-617 ◽  
Author(s):  
Xinong Li ◽  
Jiandong Wang ◽  
Biao Huang ◽  
Sien Lu
Keyword(s):  
Time Series ◽  
Detection Method ◽  
Single Time ◽  
Oscillation Detection

scholarly journals Detecting Brain State Changes via Fiber-Centered Functional Connectivity Analysis

2012 ◽  
Vol 11 (2) ◽  
pp. 193-210 ◽  
Author(s):  
Xiang Li ◽  
Chulwoo Lim ◽  
Kaiming Li ◽  
Lei Guo ◽  
Tianming Liu
Keyword(s):  
Functional Connectivity ◽  
Brain State ◽  
Connectivity Analysis ◽  
Functional Connectivity Analysis ◽  
State Changes

CHANGES IN ELECTROENCEPHALOGRAM PATTERN IN RIGHT- AND LEFT-HANDERS DURING IMAGINARY MOVEMENTS

2019 ◽  
pp. 85-92
Author(s):  
K.A. Morenova ◽  
O.A. Vedyasova
Keyword(s):  
Hemispheric Asymmetry ◽  
Alpha Rhythm ◽  
Right Hemisphere ◽  
Eeg Rhythms ◽  
Complex Motor ◽  
Depression Level ◽  
Eeg Alpha ◽  
The Right ◽  
Electroencephalogram Eeg ◽  
Eeg Pattern

The objective of the paper is to analyze changes in electroencephalogram (EEG) pattern in right- and left-handers during imaginary movements of leading and non-leading legs. Materials and Methods. The authors examined 40 right-handers and 20 left-handers, whose motor domination profile was determined by standard tests. EEGs were recorded at rest and during imaginary rotational movements of the right and left legs. NVX 36 digital DC EEG neurovisor, “10–20” system, was used for this perpose. The authors also analyzed the amplitude of the EEG rhythms (standard frequency ranges), evaluated changes in hemispheric asymmetry coefficients of frontal and central alpha and beta1 rhythms as EEG correlates of imaginary activity. Results. Imaginary movements caused a decrease in the amplitude of alpha, beta1, theta and delta rhythms on EEG in all trial subjects. The intensity of observed changes in right- and left-handers differed in symmetrical leads. As for frontal and central alpha and beta1 rhythms, which marked changes in the EEG pattern during imaginary movements, right-handers demonstrated that their depression level in symmetric cortical areas depended significantly on the imaginary movements of leading and non-leading legs and dominated in left or right hemispheres, respectively. For left-handers, changes in alpha and beta1 waves during imaginary movements of leading and non-leading legs did not differ significantly and in both cases dominated in the right hemisphere. Conclusion. Thus, the intra- and inter-hemispheric dynamics of the EEG pattern, while forming the concept and program of complex motor leg actions in left-handers, is alternative in comparison to right-handers. Keywords: EEG, alpha rhythm, beta1 rhythm, imaginary leg movements, hemispheric asymmetry, right-handers, left-handers.

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