scholarly journals A backward encoding approach to recover subcortical auditory activity

2019 ◽  
Author(s):  
Fabian Schmidt ◽  
Gianpaolo Demarchi ◽  
Florian Geyer ◽  
Nathan Weisz
Keyword(s):  
Signal To Noise Ratio ◽  
Auditory Pathway ◽  
Auditory Brainstem ◽  
Signal To Noise ◽  
Spatial Filters ◽  
Data Set ◽  
Stimulation Rate ◽  
Spatial Coverage ◽  
Brainstem Response ◽  
Noise Ratio

1.AbstractSeveral subcortical nuclei along the auditory pathway are involved in the processing of sounds. One of the most commonly used methods of measuring the activity of these nuclei is the auditory brainstem response (ABR). Due to its low signal-to-noise ratio, ABR’s have to be derived by averaging over thousands of artificial sounds such as clicks or tone bursts. This approach cannot be easily applied to natural listening situations (e.g. speech, music), which limits auditory cognitive neuroscientific studies to investigate mostly cortical processes.We propose that by training a backward encoding model to reconstruct evoked ABRs from high-density electrophysiological data, spatial filters can be tuned to auditory brainstem activity. Since these filters can be applied (i.e. generalized) to any other data set using the same spatial coverage, this could allow for the estimation of auditory brainstem activity from any continuous sensor level data. In this study, we established a proof-of-concept by using a backward encoding model generated using a click stimulation rate of 30 Hz to predict ABR activity recorded using EEG from an independent measurement using a stimulation rate of 9 Hz. We show that individually predicted and measured ABR’s are highly correlated (r ∼ 0.7). Importantly these predictions are stable even when applying the trained backward encoding model to a low number of trials, mimicking a situation with an unfavorable signal-to-noise ratio. Overall, this work lays the necessary foundation to use this approach in more interesting listening situations.

scholarly journals High Radio Frequency Studies of Equatorial and Southern AGN

1996 ◽  
Vol 175 ◽  
pp. 99-100
Author(s):  
M. Tornikoski ◽  
E. Valtaoja
Keyword(s):  
Radio Frequency ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Data Set ◽  
Noise Ratio

The Swedish-ESO Submillimetre Telescope (SEST) has been used for the high radio frequency observations of our group's AGN monitoring projects since the end of 1987.Our SEST results from October 1987 until June 1994 will be published in A&AS (in press); the data will be available electronically. The data set consists of 155 sources with the signal-to-noise -ratio of at least one observation (at 90 or 230 GHz) ≥ 4.

scholarly journals Noise reduction with reflection supervirtual interferometry

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. V249-V256
Author(s):  
Kai Lu ◽  
Zhaolun Liu ◽  
Sherif Hanafy ◽  
Gerard Schuster
Keyword(s):  
Noise Reduction ◽  
Field Data ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Data Set ◽  
Reflection Data ◽  
The Poor ◽  
The Earth ◽  
The Common ◽  
Noise Ratio

To image deeper portions of the earth, geophysicists must record reflection data with much greater source-receiver offsets. The problem with these data is that the signal-to-noise ratio (S/N) significantly diminishes with greater offset. In many cases, the poor S/N makes the far-offset reflections imperceptible on the shot records. To mitigate this problem, we have developed supervirtual reflection interferometry (SVI), which can be applied to far-offset reflections to significantly increase their S/N. The key idea is to select the common pair gathers where the phases of the correlated reflection arrivals differ from one another by no more than a quarter of a period so that the traces can be coherently stacked. The traces are correlated and summed together to create traces with virtual reflections, which in turn are convolved with one another and stacked to give the reflection traces with much stronger S/Ns. This is similar to refraction SVI except far-offset reflections are used instead of refractions. The theory is validated with synthetic tests where SVI is applied to far-offset reflection arrivals to significantly improve their S/N. Reflection SVI is also applied to a field data set where the reflections are too noisy to be clearly visible in the traces. After the implementation of reflection SVI, the normal moveout velocity can be accurately picked from the SVI-improved data, leading to a successful poststack migration for this data set.

scholarly journals Towards improved analysis of short mesoscale sea level signals from satellite altimetry

2021 ◽  
Author(s):  
Yves Quilfen ◽  
Jean-François Piolle ◽  
Bertrand Chapron
Keyword(s):  
Sea Level ◽  
Signal To Noise Ratio ◽  
Ocean Dynamics ◽  
Small Scale ◽  
Noise Filtering ◽  
Signal To Noise ◽  
Data Set ◽  
Noise Ratio ◽  
Filter Noise ◽  
Along Track

Abstract. Satellite altimeters routinely supply sea surface height (SSH) measurements, which are key observations for monitoring ocean dynamics. However, below a wavelength of about 70 km, along-track altimeter measurements are often characterized by a dramatic drop in signal-to-noise ratio, making it very challenging to fully exploit the available altimeter observations to precisely analyze small mesoscale variations in SSH. Although various approaches have been proposed and applied to identify and filter noise from measurements, no distinct methodology has emerged for systematic application in operational products. To best address this unresolved issue, the Copernicus Marine Environment Monitoring Service (CMEMS) actually provides simple band-pass filtered data to mitigate noise contamination of along-track SSH signals. More innovative and suitable noise filtering methods are thus left to users seeking to unveil small-scale altimeter signals. As demonstrated here, a fully data-driven approach is developed and applied successfully to provide robust estimates of noise-free Sea Level Anomaly (SLA) signals. The method combines Empirical Mode Decomposition (EMD), to help analyze non-stationary and non-linear processes, and an adaptive noise filtering technique inspired by Discrete Wavelet Transform (DWT) decompositions. It is found to best resolve the distribution of SLA variability in the 30–120 km mesoscale wavelength band. A practical uncertainty variable is attached to the denoised SLA estimates that accounts for errors related to the local signal-to-noise ratio, but also for uncertainties in the denoising process, which assumes that the SLA variability results in part from a stochastic process. For the available period, measurements from the Jason-3, Sentinel-3 and Saral/AltiKa missions are processed and analyzed, and their energy spectral and seasonal distributions characterized in the small mesoscale domain. In anticipation of the upcoming SWOT (Surface Water and Ocean Topography) mission data, the SASSA data set (Satellite Altimeter Short-scale Signals Analysis, Quilfen and Piolle, 2021) of denoised SLA measurements for three reference altimeter missions already yields valuable opportunities to evaluate global small mesoscale kinetic energy distributions.

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