On-line auditory brainstem response signal-to-noise ratio estimation for response detection

2005 ◽  
Author(s):  
R.E. Delgado ◽  
O. Ozdamar ◽  
R.E. Eilers
Keyword(s):  
Auditory Brainstem Response ◽  
Signal To Noise Ratio ◽  
Auditory Brainstem ◽  
Ratio Estimation ◽  
Response Signal ◽  
Signal To Noise ◽  
Brainstem Response ◽  
On Line ◽  
Response Detection ◽  
Noise Ratio

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.

Frame-Level Signal-to-Noise Ratio Estimation Using Deep Learning

2020 ◽  
Author(s):  
Hao Li ◽  
DeLiang Wang ◽  
Xueliang Zhang ◽  
Guanglai Gao
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
Ratio Estimation ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals Signal-to-Noise Ratio Estimation in Passive Correlation-Based Imaging

2013 ◽  
Vol 6 (2) ◽  
pp. 1092-1110 ◽  
Author(s):  
Josselin Garnier ◽  
George Papanicolaou ◽  
Adrien Semin ◽  
Chrysoula Tsogka
Keyword(s):  
Signal To Noise Ratio ◽  
Ratio Estimation ◽  
Signal To Noise ◽  
Noise Ratio
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