Statistical Analysis of Very Low Frequency Atmospheric Noise Caused by the Global Lightning using Ground‐based Observations in China

2021 ◽  
Author(s):  
Xudong Gu ◽  
Guangjian Li ◽  
Hong Pang ◽  
Shiwei Wang ◽  
Binbin Ni ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Low Frequency ◽  
Very Low Frequency ◽  
Atmospheric Noise

scholarly journals Analysis of Whistlers Registration Data Obtained by Ssan System on Subauroral Station in Yakutsk

2018 ◽  
Vol 62 ◽  
pp. 02004
Author(s):  
Rustam Karimov ◽  
Vladimir Mochalov ◽  
Anastasia Mochalova ◽  
Lena Tarabukina ◽  
Vyacheslav Argunov ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Signal Analysis ◽  
Low Frequency ◽  
Operational Mode ◽  
Sensor Signal ◽  
Opposite Hemisphere ◽  
Very Low Frequency ◽  
Registration Data ◽  
Geophysical Processes ◽  
Magnetosphere Plasma

Since from November 2017 to August 2018, synchronous registration of atmospherics and whistlers at the radiophysical station “Oibenkyol”, began in the operational mode within the network SSAN/VLF (“Sensor signal analysis network/Very low frequency”). The classification and the characteristics of the registered whistlers are carried out. The correlation analysis of the number of the whistlers registered by the SSAN/VLF with the number of lightning registered by the WWLLN is used to determine the location of lightning sources of whistlers in the opposite hemisphere in the magnetoconjugate point at South of Australia. Thus, the SSAN/VLF make possible the distance monitoring of the dynamics of the various geophysical processes changes. The large number of registered nose whistlers allow performing the statistical analysis of processes occurring in the magnetosphere’ plasma.

The Attenuation of Very Low Frequency Brain Oscillations in Transitions from a Rest State to Active Attention

2009 ◽  
Vol 23 (4) ◽  
pp. 191-198 ◽  
Author(s):  
Suzannah K. Helps ◽  
Samantha J. Broyd ◽  
Christopher J. James ◽  
Anke Karl ◽  
Edmund J. S. Sonuga-Barke
Keyword(s):  
Brain Activity ◽  
Sampling Rate ◽  
Low Frequency ◽  
Future Research ◽  
Adhd Symptoms ◽  
Default Mode ◽  
Very Low Frequency ◽  
Wide Range ◽  
Interference Hypothesis ◽  
Mode Interference

Background: The default mode interference hypothesis ( Sonuga-Barke & Castellanos, 2007 ) predicts (1) the attenuation of very low frequency oscillations (VLFO; e.g., .05 Hz) in brain activity within the default mode network during the transition from rest to task, and (2) that failures to attenuate in this way will lead to an increased likelihood of periodic attention lapses that are synchronized to the VLFO pattern. Here, we tested these predictions using DC-EEG recordings within and outside of a previously identified network of electrode locations hypothesized to reflect DMN activity (i.e., S3 network; Helps et al., 2008 ). Method: 24 young adults (mean age 22.3 years; 8 male), sampled to include a wide range of ADHD symptoms, took part in a study of rest to task transitions. Two conditions were compared: 5 min of rest (eyes open) and a 10-min simple 2-choice RT task with a relatively high sampling rate (ISI 1 s). DC-EEG was recorded during both conditions, and the low-frequency spectrum was decomposed and measures of the power within specific bands extracted. Results: Shift from rest to task led to an attenuation of VLFO activity within the S3 network which was inversely associated with ADHD symptoms. RT during task also showed a VLFO signature. During task there was a small but significant degree of synchronization between EEG and RT in the VLFO band. Attenuators showed a lower degree of synchrony than nonattenuators. Discussion: The results provide some initial EEG-based support for the default mode interference hypothesis and suggest that failure to attenuate VLFO in the S3 network is associated with higher synchrony between low-frequency brain activity and RT fluctuations during a simple RT task. Although significant, the effects were small and future research should employ tasks with a higher sampling rate to increase the possibility of extracting robust and stable signals.

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