Brain activity detection by estimating the signal-to-noise ratio of fMRI time series using dynamic linear models

2015 ◽  
Vol 47 ◽  
pp. 205-211
Author(s):  
Alicia Quirós ◽  
Simon P. Wilson ◽  
Raquel Montes Diez ◽  
Ana Beatriz Solana ◽  
Juan Antonio Hernández Tamames
2020 ◽  
Author(s):  
Yufeng Hu

<p>The ground surface over permafrost area subsides and uplifts annually due to the seasonal thawing and freezing of active layer. GPS Interferometric Reflectometry (GPS-IR) has been successfully applied to the signal-to-noise ratio (SNR) observations to retrieve elevation changes of frozen ground surface at Barrow, Alaska. In this study, the method is extended to include GLONASS and Galileo SNR observations. Based on the multiple SNR observations collected by SG27 in Barrow, the multiple GNSS-IR time series of ground surface elevation changes during snow-free days from late June to middle October in year 2018 are obtained at daily intervals. All the three time series show a similar pattern that the ground subsided in thaw season followed by uplifts in freezing season, which is well characterized by the previous composite physical model using thermal indexes. Fitted with the composite model, the amplitude of the GPS-derived elevation changes during the snow-free days is suggested to be 3.3 ± 0.2 cm. However, the time series of GLONASS-IR and Galileo-IR measurements are much noisier than that of GPS-IR due to their inconsistent daily satellite tracks. Applied with a specific strategy in the composite model fitting, the amplitudes of GLONASS- and Galileo-derived elevation changes are estimated to be 4.0 ± 0.3 cm and 3.9 ± 0.5 cm, respectively. Then, GLONASS-IR and Galileo-IR time series are reconstructed in turn with the fitting coefficients. Moreover, the occurrences of the short-term variations in time series of GNSS-IR measurements are found to coincidence with the precipitation events, indicating the hydrologic control on the movements of frozen ground surface. The results presented in this study show the feasibility to combine multiple GNSS to densely monitor frozen ground surface deformations, and provide an insight to understand the impacts of both thermal and hydrologic forces on the frozen ground dynamics.</p>


1998 ◽  
Vol 185 ◽  
pp. 227-228
Author(s):  
V.G. Gavryusev ◽  
E.A. Gavryuseva

We used the measurements of solar oscillations taken by GONG and GOLF experiments. The first set of data are the integrated images obtained from the complex GONG observations taken from June 10 of 1995 to January 7 of 1997, 578 days in total, referenced below as ts0 time series. Radial, dipole and quadrupole modes are well visible in this time series. The second data set is the GOLF time series obtained on-board SOHO mission from April 11, 1996 to June 22, 1997. GOLF observes the “Sun as a star”. This time series is similar to ts0 of GONG but of a better quality (better signal-to-noise ratio; uniform, practically uninterrupted data). Both experiments are significantly overlapped in time. Because of this the direct comparison between them is possible, and the effects visible in both observations support each other.


2015 ◽  
Vol 11 (A29A) ◽  
pp. 201-201
Author(s):  
Laurent Eyer ◽  
Jean-Marc Nicoletti ◽  
Stephan Morgenthaler

AbstractDiverse variable phenomena in the Universe are periodic. Astonishingly many of the periodic signals present in stars have timescales coinciding with human ones (from minutes to years). The periods of signals often have to be deduced from time series which are irregularly sampled and sparse, furthermore correlations between the brightness measurements and their estimated uncertainties are common. The uncertainty on the frequency estimation is reviewed. We explore the astronomical and statistical literature, in both cases of regular and irregular samplings. The frequency uncertainty is depending on signal to noise ratio, the frequency, the observational timespan. The shape of the light curve should also intervene, since sharp features such as exoplanet transits, stellar eclipses, raising branches of pulsation stars give stringent constraints. We propose several procedures (parametric and nonparametric) to estimate the uncertainty on the frequency which are subsequently tested against simulated data to assess their performances.


2020 ◽  
Author(s):  
Frédéric Gosselin ◽  
Laurent Caplette ◽  
Valérie Daigneault ◽  
Jean-Maxime Larouche

Researchers studying the mind often rely on behavioral tasks and differences, either in stimuli or in brain activity, between correct and incorrect trials. However, subjects often guess when they don't know the answer, leading to correct responses that result from the same causes as the incorrect responses: this is a source of noise that remains no matter the number of trials performed by the subjects. This paper presents a response reclassification procedure to reduce the noise caused by “false” correct responses using an independent source of reclassification evidence. We illustrate the procedure on data from Faghel-Soubeyrand et al. (2019) with response times as reclassification evidence. The reclassification procedure increased signal-to-noise ratio by about 13.5% with little bias. Matlab and Python implementations of the reclassification procedure are freely available.


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