scholarly journals Data-driven derivation of natural EEG frequency components: An optimised example assessing resting EEG in healthy ageing

2019 ◽  
Vol 321 ◽  
pp. 1-11 ◽  
Author(s):  
Robert J. Barry ◽  
Frances M. De Blasio ◽  
Diana Karamacoska
Psychiatry ◽  
2020 ◽  
Vol 18 (2) ◽  
pp. 39-45
Author(s):  
E. V. Damyanovich ◽  
E. V. Iznak ◽  
I. V. Oleichik ◽  
A. F. Iznak

Background: the study of clinical and neurophysiological aspects of non-suicidal self-injurious behavior (NSSI), as one of the forms and risk factors for suicidal behavior in adolescents, including those suffering from mental disorders, is an urgent medical and social scientific task. Objective: To identify the features of EEG in depressive adolescent females with NSSI compared with EEG of age norm. Patients and methods: the study included 60 female patients aged 16–25 years with NSSI in the structure of endogenous depressive conditions, and 20 healthy subjects of the same gender and age. Clinical, psychopathological, psychometric, neurophysiological and statistical methods were used. Topographic EEG mapping revealed differences in the background EEG quantitative parameters of two studied groups. Results and discussion: spectral power values of alpha-2 (9–11 Hz) and alpha-1 (8–9 Hz) EEG frequency components in occipital-parietal and temporal leads, of theta-2 activity (6–8 Hz) in central-parietal leads, as well as of delta activity (2–4 Hz) in frontal and anterior temporal leads were higher in the left hemisphere, reflected increased activation of the right hemisphere. Generalized bilaterally synchronous alpha-theta bursts were registered regularly in EEG of NSSI patients, as well. Conclusions: the spatial distribution of EEG frequency components in depressive patients with NSSI indicates relatively decreased functional state of the cortex, especially of the left hemisphere and of its anterior regions, responsible for voluntary control of activity, with higher level of activation of temporal regions of the right hemisphere, associated with formation of negative emotions, and increased excitability of brain limbic-diencephalic structures, that may underlie poor controlled impulsive behavior.


2019 ◽  
Vol 139 (5) ◽  
pp. 588-595
Author(s):  
Akihiko Tsukahara ◽  
Masayuki Yamada ◽  
Keita Tanaka ◽  
Yoshinori Uchikawa

2012 ◽  
Vol 433-440 ◽  
pp. 6927-6934
Author(s):  
Da Zhuang Chen ◽  
Jia Dong Huang ◽  
Yang Sun

Empirical mode decomposition (EMD), which is the core mechanic of the Hilbert-Huang transform(HHT), is a local, fully data driven and self-adaptive analysis approach. It is a powerful tool for analyzing multi-component signals. Aiming at the reduction of scale mixing and artificial frequency components, an improved scheme was proposed for analysis and reconstruction of nonstationary and multicomponent signals. The improved EMD method uses the wavelet analysis method and normalized correlation coefficient to deal with the problems. Because the inrush current is a peaked wave with nonstationary component, a new algorithm based on improved EMD is presented for fast discrimination between inrush current and fault current of power transformers. Theoretical analysis and dynamic simulation results show that the method is effective and reliable under various fault conditions and simple to be applied.


2019 ◽  
Vol 88 (11-12) ◽  
pp. 1155-1173 ◽  
Author(s):  
Natasza Marrouch ◽  
Joanna Slawinska ◽  
Dimitrios Giannakis ◽  
Heather L. Read

Abstract This article presents a novel, nonlinear, data-driven signal processing method, which can help neuroscience researchers visualize and understand complex dynamical patterns in both time and space. Specifically, we present applications of a Koopman operator approach for eigendecomposition of electrophysiological signals into orthogonal, coherent components and examine their associated spatiotemporal dynamics. This approach thus provides enhanced capabilities over conventional computational neuroscience tools restricted to analyzing signals in either the time or space domains. This is achieved via machine learning and kernel methods for data-driven approximation of skew-product dynamical systems. The approximations successfully converge to theoretical values in the limit of long embedding windows. First, we describe the method, then using electrocorticographic (ECoG) data from a mismatch negativity experiment, we extract time-separable frequencies without bandpass filtering or prior selection of wavelet features. Finally, we discuss in detail two of the extracted components, Beta ($ \sim $ ∼ 13 Hz) and high Gamma ($ \sim $ ∼ 50 Hz) frequencies, and explore the spatiotemporal dynamics of high- and low- frequency components.


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