Assessment visual fatigue of watching 3DTV using EEG power spectral parameters

Displays ◽  
2014 ◽  
Vol 35 (5) ◽  
pp. 266-272 ◽  
Author(s):  
Chunxiao Chen ◽  
Jing Wang ◽  
Kun Li ◽  
Qiuyi Wu ◽  
Haowen Wang ◽  
...  
Keyword(s):  
Visual Fatigue ◽  
Spectral Parameters ◽  
Power Spectral ◽  
Eeg Power

Prefrontal Cortex Acetylcholine Release, EEG Slow Waves, and Spindles Are Modulated by M2 Autoreceptors in C57BL/6J Mouse

2002 ◽  
Vol 87 (6) ◽  
pp. 2817-2822 ◽  
Author(s):  
Christopher L. Douglas ◽  
Helen A. Baghdoyan ◽  
Ralph Lydic
Keyword(s):  
Prefrontal Cortex ◽  
Slow Wave ◽  
Cortical Activation ◽  
Mouse Strains ◽  
Potential Contribution ◽  
Ach Release ◽  
Power Spectral ◽  
Eeg Data ◽  
Muscarinic Cholinergic ◽  
Eeg Power

Recent evidence suggests that muscarinic cholinergic receptors of the M2 subtype serve as autoreceptors modulating acetylcholine (ACh) release in prefrontal cortex. The potential contribution of M2 autoreceptors to excitability control of prefrontal cortex has not been investigated. The present study tested the hypothesis that M2 autoreceptors contribute to activation of the cortical electroencephalogram (EEG) in C57BL/6J (B6) mouse. This hypothesis was evaluated using microdialysis delivery of the muscarinic antagonist AF-DX116 (3 nM) while simultaneously quantifying ACh release in prefrontal cortex, number of 7- to 14-Hz EEG spindles, and EEG power spectral density. Mean ACh release in prefrontal cortex was significantly increased ( P < 0.0002) by AF-DX116. The number of 7- to 14-Hz EEG spindles caused by halothane anesthesia was significantly decreased ( P < 0.0001) by dialysis delivery of AF-DX116 to prefrontal cortex. The cholinergically induced cortical activation was characterized by a significant ( P < 0.05) decrease in slow-wave EEG power. Together, these neurochemical and EEG data support the conclusion that M2 autoreceptor enhancement of ACh release in prefrontal cortex activates EEG in contralateral prefrontal cortex of B6 mouse. EEG slow-wave activity varies across mouse strains, and the results encourage comparative phenotyping of cortical ACh release and EEG in additional mouse models.

EEG power spectral density under Propofol and its association with burst suppression, a marker of cerebral fragility

2019 ◽  
Vol 130 (8) ◽  
pp. 1311-1319 ◽  
Author(s):  
Cyril Touchard ◽  
Jérôme Cartailler ◽  
Charlotte Levé ◽  
Pierre Parutto ◽  
Cédric Buxin ◽  
...  
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Burst Suppression ◽  
Power Spectral ◽  
Eeg Power

All-night EEG power spectral analysis of the cyclic alternating pattern components in young adult subjects

2005 ◽  
Vol 116 (10) ◽  
pp. 2429-2440 ◽  
Author(s):  
Raffaele Ferri ◽  
Oliviero Bruni ◽  
Silvia Miano ◽  
Giuseppe Plazzi ◽  
Mario G. Terzano
Keyword(s):  
Spectral Analysis ◽  
Young Adult ◽  
Power Spectral Analysis ◽  
Cyclic Alternating Pattern ◽  
Power Spectral ◽  
Eeg Power

On the Characterization of Dynamic Properties of Random Processes by Spectral Parameters

2000 ◽  
Vol 67 (3) ◽  
pp. 519-526 ◽  
Author(s):  
G. Petrucci ◽  
M. Di Paola ◽  
B. Zuccarello
Keyword(s):  
Dynamic Properties ◽  
Time Derivative ◽  
Wide Band ◽  
Random Processes ◽  
Spectral Parameters ◽  
Unambiguous Determination ◽  
Power Spectral ◽  
Bandwidth Parameter

This paper deals with the general problem of directly relating the distribution of ranges of wide band random processes to the power spectral density (PSD) by means of closed-form expressions. Various attempts to relate the statistical distribution of ranges to the PSD by means of the irregularity factor or similar parameters have been done by several authors but, unfortunately, they have not been fully successful. In the present study, introducing the so-called analytic processes, the reasons for which these parameters are insufficient to an unambiguous determination of the range distribution and the fact that parameters regarding the time-derivative processes are needed have been explained. Furthermore, numerical simulations have shown that the range distributions depend on the irregularity factor and bandwidth parameter of both the process and its derivative. These observations are the basis for the determination of accurate relationships between range distributions and PSDs. [S0021-8936(00)02903-2]

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