scholarly journals Neural Spike Sorting And Classification

2020 ◽  
Vol 23 (2) ◽  
pp. 166-178
Author(s):  
Zaid H. Berjis ◽  
Ahmed K. Al-sulaifanie
Keyword(s):  
Background Noise ◽  
Euclidean Distance ◽  
Signal To Noise Ratio ◽  
Extracellular Recording ◽  
Spike Sorting ◽  
Signal To Noise ◽  
Acceptable Solution ◽  
Neural Spike ◽  
High Level ◽  
The Brain

Spike sorting is the process of separating the extracellular recording of the brain signal into one unit activity. There are a number of proposed algorithms for this purpose, but there is still no acceptable solution. In this paper a spike sorting method has been proposed based on the Euclidean distance of the most effective features of spikes represented by principle components (PCs) of the detected and aligned spikes. The assessments of the method, based on signal-to-noise ratio (SNR) representing background noise, showed that the method performed spike sorting to a high level of accuracy.

Selecting the Optimal FM System for Children With Cochlear Implants

2008 ◽  
Vol 18 (1) ◽  
pp. 19-24
Author(s):  
Erin C. Schafer
Keyword(s):  
Speech Recognition ◽  
Cochlear Implants ◽  
Empirical Research ◽  
Background Noise ◽  
Signal To Noise Ratio ◽  
Evidence Based ◽  
Signal To Noise ◽  
Speech Processor ◽  
System Input ◽  
Optimal Type

Children who use cochlear implants experience significant difficulty hearing speech in the presence of background noise, such as in the classroom. To address these difficulties, audiologists often recommend frequency-modulated (FM) systems for children with cochlear implants. The purpose of this article is to examine current empirical research in the area of FM systems and cochlear implants. Discussion topics will include selecting the optimal type of FM receiver, benefits of binaural FM-system input, importance of DAI receiver-gain settings, and effects of speech-processor programming on speech recognition. FM systems significantly improve the signal-to-noise ratio at the child's ear through the use of three types of FM receivers: mounted speakers, desktop speakers, or direct-audio input (DAI). This discussion will aid audiologists in making evidence-based recommendations for children using cochlear implants and FM systems.

scholarly journals On cross-correlations, averages and noise in electron microscopy

2019 ◽  
Vol 75 (1) ◽  
pp. 12-18 ◽  
Author(s):  
Michael Radermacher ◽  
Teresa Ruiz
Keyword(s):  
High Resolution ◽  
Cross Correlation ◽  
Signal To Noise Ratio ◽  
3D Structure ◽  
Signal To Noise ◽  
High Resolution Structure ◽  
Multiple Copies ◽  
High Level ◽  
2D Images ◽  
Resolution Structure

Biological samples are radiation-sensitive and require imaging under low-dose conditions to minimize damage. As a result, images contain a high level of noise and exhibit signal-to-noise ratios that are typically significantly smaller than 1. Averaging techniques, either implicit or explicit, are used to overcome the limitations imposed by the high level of noise. Averaging of 2D images showing the same molecule in the same orientation results in highly significant projections. A high-resolution structure can be obtained by combining the information from many single-particle images to determine a 3D structure. Similarly, averaging of multiple copies of macromolecular assembly subvolumes extracted from tomographic reconstructions can lead to a virtually noise-free high-resolution structure. Cross-correlation methods are often used in the alignment and classification steps of averaging processes for both 2D images and 3D volumes. However, the high noise level can bias alignment and certain classification results. While other approaches may be implicitly affected, sensitivity to noise is most apparent in multireference alignments, 3D reference-based projection alignments and projection-based volume alignments. Here, the influence of the image signal-to-noise ratio on the value of the cross-correlation coefficient is analyzed and a method for compensating for this effect is provided.

Speech in noise testing before and after grommet insertion

2012 ◽  
Vol 126 (10) ◽  
pp. 1010-1015 ◽  
Author(s):  
V Possamai ◽  
G Kirk ◽  
A Scott ◽  
D Skinner
Keyword(s):  
Small Group ◽  
Background Noise ◽  
Signal To Noise Ratio ◽  
Sound Field ◽  
Noise Signal ◽  
Signal To Noise ◽  
Speech In Noise ◽  
Before And After ◽  
Noise Test ◽  
Noise Ratio

AbstractObjectives:To assess the feasibility of designing and implementing a speech in noise test in children before and after grommet insertion, and to analyse the results of such a test in a small group of children.Methods:Twelve children aged six to nine years who were scheduled to undergo grommet insertion were identified. They underwent speech in noise testing before and after grommet insertion. This testing used Arthur Boothroyd word lists read at 60 dB in four listening conditions presented in a sound field: firstly in quiet conditions, then in signal to noise ratios of +10 (50 dB background noise), 0 (60 dB) and −10 (70 dB).Results:Mean phoneme scores were: in quiet conditions, 28.1 pre- and 30 post-operatively (p = 0.04); in 50 dB background noise (signal to noise ratio +10), 24.2 pre- and 29 post-operatively (p < 0.01); in 60 dB background noise (signal to noise ratio 0), 22.6 pre- and 27.5 post-operatively (p = 0.06); and in 70 dB background noise (signal to noise ratio −10), 13.9 pre- and 21 post-operatively (p = 0.05).Conclusion:This small study suggests that speech in noise testing is feasible in this scenario. Our small group of children demonstrated a significant improvement in speech in noise scores following grommet insertion. This is likely to translate into a significant advantage in the educational environment.

Accuracy of Pointing a Binaural Listening Array

1997 ◽  
Vol 39 (4) ◽  
pp. 651-658
Author(s):  
Tomasz R. Letowski ◽  
Gilbert L. Ricard ◽  
Joel T. Kalb ◽  
Timothy J. Mermagen ◽  
Kristin M. Amrein
Keyword(s):  
Background Noise ◽  
Angular Separation ◽  
Sensor System ◽  
Signal To Noise ◽  
Target Sound ◽  
Accurate Localization ◽  
Pointing Accuracy ◽  
Band Limited ◽  
High Level ◽  
Binaural Listening

We measured the accuracy with which sounds heard over a binaural, end-fire array could be located when the angular separation of the array's two arms was varied. Each individual arm contained nine cardioid electret microphones, the responses of which were combined to produce a unidirectional, band-limited pattern of sensitivity. We assessed the desirable angular separation of these arms by measuring the accuracy with which listeners could point to the source of a target sound presented against high-level background noise. We employed array separations of 30°, 45°, and 60°, and signal-to-noise ratios of +5, -5, and -15 dB. Pointing accuracy was best for a separation of 60°; this performance was indistinguishable from pointing during unaided listening conditions. In addition, the processing of the array was modeled to depict the information that was available for localization. The model indicates that highly directional binaural arrays can be expected to support accurate localization of sources of sound only near the axis of the array. Wider enhanced listening angles may be possible if the forward coverage of the sensor system is made less directional and more similar to that of human listeners.

scholarly journals Improved deconvolution of very weak confocal signals

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 787 ◽  
Author(s):  
Kasey J. Day ◽  
Patrick J. La Rivière ◽  
Talon Chandler ◽  
Vytas P. Bindokas ◽  
Nicola J. Ferrier ◽  
...  
Keyword(s):  
Background Noise ◽  
Signal To Noise Ratio ◽  
Simulated Data ◽  
Time Lapse ◽  
Confocal Image ◽  
Signal To Noise ◽  
4D Imaging ◽  
Fluorescence Images ◽  
Gaussian Blur ◽  
Imaging Conditions

Deconvolution is typically used to sharpen fluorescence images, but when the signal-to-noise ratio is low, the primary benefit is reduced noise and a smoother appearance of the fluorescent structures. 3D time-lapse (4D) confocal image sets can be improved by deconvolution. However, when the confocal signals are very weak, the popular Huygens deconvolution software erases fluorescent structures that are clearly visible in the raw data. We find that this problem can be avoided by prefiltering the optical sections with a Gaussian blur. Analysis of real and simulated data indicates that the Gaussian blur prefilter preserves meaningful signals while enabling removal of background noise. This approach is very simple, and it allows Huygens to be used with 4D imaging conditions that minimize photodamage.

Response variability of single cells in the dorsal lateral geniculate nucleus of the cat. Comparison with retinal input and effect of brain stem stimulation

1994 ◽  
Vol 72 (3) ◽  
pp. 1278-1289 ◽  
Author(s):  
E. Hartveit ◽  
P. Heggelund
Keyword(s):  
Firing Rate ◽  
Brain Stem ◽  
Signal To Noise Ratio ◽  
Single Cells ◽  
Response Variability ◽  
Dorsal Lateral Geniculate Nucleus ◽  
Signal To Noise ◽  
Retinal Input ◽  
Dorsal Lateral Geniculate ◽  
The Brain

1. We studied the degree and source of response variability in different classes of cell in the dorsal lateral geniculate nucleus (dLGN). The response of single cells to a series of contrasts of a stationary flashing light spot was measured. The variability analyses were based on the mean and SD of the response to a number of repeated stimulus presentations. The relative variability was expressed by the coefficient of variation (Cv; SD/mean). 2. At a given contrast, the Cv for lagged cells was larger than for nonlagged cells. No difference was found between the Cv of X and Y cells. The magnitude of the Cv was about the same as previously found for cells in striate cortex. Accordingly, little variability is added at the cortical level. The Cv decreased with increasing contrast showing that the reliability of response and the signal-to-noise ratio was improved with increasing contrast. 3. For some cells, the retinal input was determined by recording S potentials in addition to action potentials. The Cv of the retinal input was smaller than the Cv of the dLGN cells at a given contrast. Thus in the paralyzed and anesthetized preparation, variability was added at the geniculate relay. 4. The additional variability was related to modulatory input from the brain stem. This was shown by comparing Cv versus contrast curves for the dLGN cells obtained during electrical stimulation of the peribrachial region of the brain stem (PBR) with corresponding curves obtained without PBR stimulation. During PBR stimulation, which presumably mimics the effects of arousal on the dLGN cell, the Cv at a given contrast was reduced toward the value for the retinal input to the cell. Furthermore PBR stimulation increased the signal-to-noise-ratio of the cell to the level of the retinal input. 5. When Cv was plotted against response rather than against contrast, approximately the same function was found for the various dLGN cell classes. This indicated that the variability basically depended on firing rate rather than on stimulus contrast. No difference of Cv was seen between lagged and nonlagged cells at a given level of response. The difference found at a given level of contrast reflected differences in firing rate of the two cell classes. During PBR stimulation, there was no clear difference between the Cvs of the dLGN cell and its retinal input at a given level of response.(ABSTRACT TRUNCATED AT 400 WORDS)

scholarly journals Super-Resolution Reconstruction of Cell Pseudo-Color Image Based on Raman Technology

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4076
Author(s):  
Yang ◽  
Zhu ◽  
Wang ◽  
Yang ◽  
Wu ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Raman Spectrum ◽  
Background Noise ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Pseudo Color

Raman spectroscopy visualization is a challenging task due to the interference of complex background noise and the number of selected measurement points. In this paper, a super-resolution image reconstruction algorithm for Raman spectroscopy is studied to convert raw Raman data into pseudo-color super-resolution imaging. Firstly, the Raman spectrum data of a single measurement point is measured multiple times to calculate the mean value to remove the random background noise, and innovatively introduce the Retinex algorithm and the median filtering algorithm which improve the signal-to-noise ratio. The novel method of using deep neural network performs a super-resolution reconstruction operation on the gray image. An adaptive guided filter that automatically adjusts the filter radius and penalty factor is proposed to highlight the contour of the cell, and the super-resolution reconstruction of the pseudo-color image of the Raman spectrum is realized. The average signal-to-noise ratio of the reconstructed pseudo-color image sub-band reaches 14.29 db, and the average value of information entropy reaches 4.30 db. The results show that the Raman-based cell pseudo-color image super-resolution reconstruction algorithm is an effective tool to effectively remove noise and high-resolution visualization. The contrast experiments show that the pseudo-color image Kullback–Leiber (KL) entropy of the color image obtained by the method is small, the boundary is obvious, and the noise is small, which provide technical support for the development of sophisticated single-cell imaging Raman spectroscopy instruments.

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