Spectral Power Variation Separates Oscillatory from Non-Oscillatory Stochastic Neural Dynamics

2018 ◽  
Author(s):  
Richard Gao ◽  
Lauren Liao ◽  
Bradley Voytek
Keyword(s):  
Spectral Power ◽  
Neural Dynamics ◽  
Power Variation

scholarly journals Neural dynamics of stroboscopic stimulation at different stimulation frequencies

2021 ◽  
Author(s):  
Ram Kumar Pari
Keyword(s):  
Spectral Power ◽  
Neural Dynamics ◽  
Systematic Research ◽  
Driving Frequency ◽  
Altered States ◽  
States Of Consciousness ◽  
Signal Diversity ◽  
Experiential Content ◽  
Specific Stimulation ◽  
The Brain

Stroboscopic stimulation has been previously shown to induce visual hallucinations and altered states of consciousness, state by entraining the brain to the driving frequency, similar to those reported during the psychedelics although little systematic research exists on the effect of specific stimulation frequency on experience. The present study investigated the effects of different stroboscopic stimulation frequencies on neural dynamics, such as signal diversity (Lempel-Ziv complexity) and spectral power and attempted to relate these changes to self-reported changes in experiential content. The results indicated that the stimulation frequencies near the alpha band (8 to 12 Hz) caused the greatest increase across all neural measures, with 8 Hz consistently displaying the most pronounced results, relative to baseline. All tested frequencies led to an increase in all experiential dimensions, relative to baseline.

Associations Between Self-Report of Emotional State and the EEG Patterns in Affective Disorders Patients

2010 ◽  
Vol 24 (1) ◽  
pp. 33-40 ◽  
Author(s):  
Miroslaw Wyczesany ◽  
Jan Kaiser ◽  
Anton M. L. Coenen
Keyword(s):  
State Energy ◽  
Emotional State ◽  
Spectral Power ◽  
Bipolar Affective Disorder ◽  
Affective State ◽  
Manic Episode ◽  
High Energy ◽  
Self Report ◽  
Major Depressive ◽  
High Tension

The study determines the associations between self-report of ongoing emotional state and EEG patterns. A group of 31 hospitalized patients were enrolled with three types of diagnosis: major depressive disorder, manic episode of bipolar affective disorder, and nonaffective patients. The Thayer ADACL checklist, which yields two subjective dimensions, was used for the assessment of affective state: Energy Tiredness (ET) and Tension Calmness (TC). Quantitative analysis of EEG was based on EEG spectral power and laterality coefficient (LC). Only the ET scale showed relationships with the laterality coefficient. The high-energy group showed right shift of activity in frontocentral and posterior areas visible in alpha and beta range, respectively. No effect of ET estimation on prefrontal asymmetry was observed. For the TC scale, an estimation of high tension was related to right prefrontal dominance and right posterior activation in beta1 band. Also, decrease of alpha2 power together with increase of beta2 power was observed over the entire scalp.

Variations Across the Menstrual Cycle in EEG Activity During Thinking and Mental Relaxation

1999 ◽  
Vol 13 (3) ◽  
pp. 163-172 ◽  
Author(s):  
R. Krug ◽  
M. Mölle ◽  
H.L. Fehm ◽  
J. Born
Keyword(s):  
Menstrual Cycle ◽  
Divergent Thinking ◽  
Spectral Power ◽  
Plasma Concentrations ◽  
Task Demands ◽  
Menstrual Phase ◽  
Convergent Thinking ◽  
Eeg Activity ◽  
Dimensional Complexity ◽  
Mental Relaxation

Abstract Previous studies have indicated: (1) peak performance on tests of divergent creative thinking during the ovulatory phase of the menstrual cycle; (2) compared to convergent analytical thinking, divergent thinking was found to be associated with a distinctly increased dimensional complexity of ongoing EEG activity. Based on these findings, we hypothesized that cortical information processing during the ovulatory phase is characterized by an increased EEG dimensionality. Each of 16 women was tested on 3 occasions: during the ovulatory phase, the luteal phase, and menses. Presence of the phases was confirmed by determination of plasma concentrations of estradiol, progesterone, and luteinizing hormone. The EEG was recorded while the women performed: (1) tasks of divergent thinking; (2) tasks of convergent thinking; and (3) during mental relaxation. In addition to EEG dimensional complexity, conventional spectral power analysis was performed. Behavioral data confirmed enhanced creative performance during the ovulatory phase while convergent thinking did not vary across cycle phases. EEG complexity was higher during divergent than convergent thought, but this difference remained unaffected by the menstrual phase. Influences of the menstrual phase on EEG activity were most obvious during mental relaxation. In this condition, women during the ovulatory phase displayed highest EEG dimensionality as compared with the other cycle phases, with this effect being most prominent over the central and parietal cortex. Concurrently, power within the alpha frequency band as well as theta power at frontal and parietal leads were lower during the luteal than ovulatory phase. EEG results indicate that task demands of thinking overrode effects of menstrual cycle. However, with a less demanding situation, an ovulatory increase in EEG dimensionality became prominent suggesting a loosening of associative habits during this phase.

Supercontinuum laser-based monochromatic radiant source for detector spectral responsivity characterization in the range 0.9–1.6 μm using absolute cryogenic radiometer

2020 ◽  
pp. 28-33
Author(s):  
A. Yu. Dunaev ◽  
A. S. Baturin ◽  
V. N. Krutikov ◽  
S. P. Morozova
Keyword(s):  
Power Distribution ◽  
Spectral Power ◽  
Feedback System ◽  
Operating Mode ◽  
Spectral Responsivity ◽  
Spectral Power Distribution ◽  
Cryogenic Radiometer ◽  
Supercontinuum Laser ◽  
Radiant Power ◽  
Double Monochromator

An improved monochromatic radiant source with spectral bandwidth of 4 nm based on supercontinuum laser and a double monochromator was included in absolute cryogenic radiometer-based facility to improve the accuracy of spectral responsivity measurement in the range 0.9–1.6 μm. The developed feedback system ensures stabilization of monochromatic radiant power with standard deviation up to 0.025 %. Radiant power that proceeds detector under test or absolute cryogenic radiometer varies from 0.1 to 1.5 mW in dependence of wavelength. The spectral power distribution of its monochromatic source for various operating mode is presented.

Inflections of Periodicities of Background Photon Spectral Power Densities Are Predicted by the Lorentz Contraction for a Potential Indicator for the Intrinsic Structure of Space

2016 ◽  
pp. 4014-4017
Author(s):  
Michael A Persinger
Keyword(s):  
Spectral Power ◽  
Neutral Hydrogen ◽  
Intrinsic Structure ◽  
Lorentz Contraction ◽  
Intrinsic Nature ◽  
Basic Interval ◽  
Power Spectral ◽  
The Difference ◽  
Background Photon ◽  
Power Densities

                The value for the Lorentz contraction to produce a discrepancy for a hypothetical number that reflects a property (21.3π4) of sub-matter space was calculated. When applied to time the contraction would be ~35 min. The difference in mass-equivalent energy for an electron at c (the velocity of light in a vacuum) and the required v was ~2 ·10-20 J which has emerged as a significant quantity that may permeate from the force at Planck’s Length when applied across the wavelength of the neutral hydrogen line. Two separate types of photomultiplier instruments (digital and analogue) measuring with different sampling rates for background photon quantities over 50 randomly selected days demonstrated averaged conspicuous inflections of standardized spectral power densities around 35 min. This is the same basic interval where microvariations in the value of the gravitational constant (G) approached a limit at which white noise dominated.  The possibility is considered that this value for temporal inflections in photon power spectral densities may reflect the intrinsic nature of space-time contractions that relate gravity and photons.

Behavioural and EEG atypicalities during rest, visual perception, and cognitive control in autistic adults

2020 ◽  
Author(s):  
Amandine Lassalle ◽  
Michael X Cohen ◽  
Laura Dekkers ◽  
Elizabeth Milne ◽  
Rasa Gulbinaite ◽  
...  
Keyword(s):  
Visual Perception ◽  
Cognitive Control ◽  
Visual Processing ◽  
Autism Spectrum ◽  
Neural Dynamics ◽  
Simple Explanation ◽  
Control Task ◽  
Neural Structure ◽  
Eeg Power ◽  
Autistic Adults

Background: People with an Autism Spectrum Condition diagnosis (ASD) are hypothesized to show atypical neural dynamics, reflecting differences in neural structure and function. However, previous results regarding neural dynamics in autistic individuals have not converged on a single pattern of differences. It is possible that the differences are cognitive-set-specific, and we therefore measured EEG in autistic individuals and matched controls during three different cognitive states: resting, visual perception, and cognitive control.Methods: Young adults with and without an ASD (N=17 in each group) matched on age (range 20 to 30 years), sex, and estimated Intelligence Quotient (IQ) were recruited. We measured their behavior and their EEG during rest, a task requiring low-level visual perception of gratings of varying spatial frequency, and the “Simon task” to elicit activity in the executive control network. We computed EEG power and Inter-Site Phase Clustering (ISPC; a measure of connectivity) in various frequency bands.Results: During rest, there were no ASD vs. controls differences in EEG power, suggesting typical oscillation power at baseline. During visual processing, without pre-baseline normalization, we found decreased broadband EEG power in ASD vs. controls, but this was not the case during the cognitive control task. Furthermore, the behavioral results of the cognitive control task suggest that autistic adults were better able to ignore irrelevant stimuli.Conclusions: Together, our results defy a simple explanation of overall differences between ASD and controls, and instead suggest a more nuanced pattern of altered neural dynamics that depend on which neural networks are engaged.

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