scholarly journals ION-Decoding: A Single-channel Interactive Offline Neural Decoding Algorithm for a large number of neurons

2020 ◽  
Author(s):  
Mohsen Rastegari ◽  
Hamid Reza Marateb
Keyword(s):  
Single Channel ◽  
Signal To Noise Ratio ◽  
Sorting Algorithm ◽  
Spike Sorting ◽  
Decoding Algorithm ◽  
False Positive Error ◽  
Extracellular Recordings ◽  
Health Technologies ◽  
Cognitive Studies ◽  
Minimum Number

AbstractResearchers have widely used extracellular recordings as a technique of paramount importance due to its wide usage in cognitive studies, health technologies, and prosthetics and orthotics research. To extract the required information from this technique, a critical and crucial step, called spike sorting, must be performed on the recorded signal. By this method, it is possible to analyze a single neuron (single-unit activity) and investigate its specifications, such as the firing rates and the number of action potentials (spikes) of an individual neuron. Here we introduce a novel idea of a user-friendly interactive, offline, and unsupervised algorithm called ION-Decoding. This platform extracts and aligns the spikes using a high-resolution alignment method, and the clusters can be atomically identified and manually edited. The entire procedure is performed using the minimum number of adjustable parameters, and cluster merging was performed in a smart, intuitive way. The ION-Decoding algorithm was evaluated by a benchmark dataset, including 95 simulations of two to twenty neurons from 10 minutes simulated extracellular recordings. There was not any significant relationship between the number of missed clusters with the quality of the signal (i.e., the signal-to-noise ratio (SNR)) by controlling the number of neurons in each signal (p_value=0.103). Moreover, the number of extra clusters was not significantly dependent on the parameter SNR (p_value=0.400). The accuracy of the classification method was significantly associated with the decomposability index (DI) (p_value<0.001). A number of 77% of the neurons with the DI higher than 20 had the classification accuracy higher than 80%. The ION-Decoding algorithm significantly outperformed Wave_Clus in terms of the number of hits (p_value=0.017). However, The Wave_Clus algorithm significantly outperformed the ION-Decoding algorithm when the false-positive error (FP) was considered (p_value=0.001). The ION-Decoding is thus a promising single-channel spike sorting algorithm. However, our future focuses on the improvement of the cluster representative identification and FP error reduction.

scholarly journals A novel and fully automatic spike-sorting implementation with variable number of features

2018 ◽  
Vol 120 (4) ◽  
pp. 1859-1871 ◽  
Author(s):  
Fernando J. Chaure ◽  
Hernan G. Rey ◽  
Rodrigo Quian Quiroga
Keyword(s):  
Single Channel ◽  
Feature Selection Method ◽  
Simulated Data ◽  
Variable Number ◽  
False Positives ◽  
Sorting Algorithm ◽  
Spike Sorting ◽  
Fully Automatic ◽  
Multiple Clusters ◽  
Tetrode Recordings

The most widely used spike-sorting algorithms are semiautomatic in practice, requiring manual tuning of the automatic solution to achieve good performance. In this work, we propose a new fully automatic spike-sorting algorithm that can capture multiple clusters of different sizes and densities. In addition, we introduce an improved feature selection method, by using a variable number of wavelet coefficients, based on the degree of non-Gaussianity of their distributions. We evaluated the performance of the proposed algorithm with real and simulated data. With real data from single-channel recordings, in ~95% of the cases the new algorithm replicated, in an unsupervised way, the solutions obtained by expert sorters, who manually optimized the solution of a previous semiautomatic algorithm. This was done while maintaining a low number of false positives. With simulated data from single-channel and tetrode recordings, the new algorithm was able to correctly detect many more neurons compared with previous implementations and also compared with recently introduced algorithms, while significantly reducing the number of false positives. In addition, the proposed algorithm showed good performance when tested with real tetrode recordings. NEW & NOTEWORTHY We propose a new fully automatic spike-sorting algorithm, including several steps that allow the selection of multiple clusters of different sizes and densities. Moreover, it defines the dimensionality of the feature space in an unsupervised way. We evaluated the performance of the algorithm with real and simulated data, from both single-channel and tetrode recordings. The proposed algorithm was able to outperform manual sorting from experts and other recent unsupervised algorithms.

Information capacity and bandwidth limitations in the SEM

1989 ◽  
Vol 47 ◽  
pp. 84-85
Author(s):  
D. C. Joy ◽  
R. D. Bunn
Keyword(s):  
Signal To Noise Ratio ◽  
Critical Dimension ◽  
Information Capacity ◽  
Acquisition Time ◽  
Signal To Noise ◽  
Sem Image ◽  
Probe Size ◽  
Minimum Number ◽  
Noise Ratio ◽  
Signal Channel

The information available from an SEM image is limited both by the inherent signal to noise ratio that characterizes the image and as a result of the transformations that it may undergo as it is passed through the amplifying circuits of the instrument. In applications such as Critical Dimension Metrology it is necessary to be able to quantify these limitations in order to be able to assess the likely precision of any measurement made with the microscope.The information capacity of an SEM signal, defined as the minimum number of bits needed to encode the output signal, depends on the signal to noise ratio of the image - which in turn depends on the probe size and source brightness and acquisition time per pixel - and on the efficiency of the specimen in producing the signal that is being observed. A detailed analysis of the secondary electron case shows that the information capacity C (bits/pixel) of the SEM signal channel could be written as :

scholarly journals Minimum Array Elements for Resolution of Several Direction of Arrival Estimation Methods in Various Noise-Level Environments

2018 ◽  
Vol 2 ◽  
pp. 87-94
Author(s):  
Ismail El Ouargui ◽  
Said Safi ◽  
Miloud Frikel
Keyword(s):  
Gaussian Noise ◽  
Noise Level ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Estimation Methods ◽  
Minimum Number ◽  
Informative Signals

The resolution of a Direction of Arrival (DOA) estimation algorithm is determined based on its capability to resolve two closely spaced signals. In this paper, authors present and discuss the minimum number of array elements needed for the resolution of nearby sources in several DOA estimation methods. In the real world, the informative signals are corrupted by Additive White Gaussian Noise (AWGN). Thus, a higher signal-to-noise ratio (SNR) offers a better resolution. Therefore, we show the performance of each method by applying the algorithms in different noise level environments.

scholarly journals Now you see it, now you don't: optimal parameters for interslice stimulation in concurrent TMS-fMRI

2021 ◽  
Author(s):  
Catriona L Scrivener ◽  
Jade B Jackson ◽  
Marta Morgado Correia ◽  
Marius Mada ◽  
Alexandra Woolgar
Keyword(s):  
Single Channel ◽  
Signal To Noise Ratio ◽  
Brain Activity ◽  
Surface Coil ◽  
Acquisition Time ◽  
Surface Coils ◽  
Human Participant ◽  
Set Up ◽  
Promising Avenue ◽  
Slice Acquisition

The powerful combination of transcranial magnetic stimulation (TMS) concurrent with functional magnetic resonance imaging (fMRI) provides rare insights into the causal relationships between brain activity and behaviour. Despite a recent resurgence in popularity, TMS-fMRI remains technically challenging. Here we examined the feasibility of applying TMS during short gaps between fMRI slices to avoid incurring artefacts in the fMRI data. We quantified signal dropout and changes in temporal signal-to-noise ratio (tSNR) for TMS pulses presented at timepoints from 100ms before to 100ms after slice onset. Up to 3 pulses were delivered per volume using MagVenture's MR-compatible TMS coil. We used a spherical phantom, two 7-channel TMS-dedicated surface coils, and a multiband (MB) sequence (factor=2) with interslice gaps of 100ms and 40ms, on a Siemens 3T Prisma-fit scanner. For comparison we repeated a subset of parameters with a more standard single-channel TxRx (birdcage) coil, and with a human participant and surface coil set up. We found that, even at 100% stimulator output, pulses applied at least -40ms/+50ms from the onset of slice readout avoid incurring artifacts. This was the case for all three setups. Thus, an interslice protocol can be achieved with a frequency of up to ~10 Hz, using a standard EPI sequence (slice acquisition time: 62.5ms, interslice gap: 40ms). Faster stimulation frequencies would require shorter slice acquisition times, for example using in-plane acceleration. Interslice TMS-fMRI protocols provide a promising avenue for retaining flexible timing of stimulus delivery without incurring TMS artifacts.

scholarly journals Degrees-Of-Freedom in Multi-Cloud Based Sectored Cellular Networks

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 668
Author(s):  
Samet Gelincik ◽  
Ghaya Rekaya-Ben Othman
Keyword(s):  
Cellular Networks ◽  
Degrees Of Freedom ◽  
Signal To Noise Ratio ◽  
Base Stations ◽  
Processing Capacity ◽  
Coding Schemes ◽  
Minimum Number ◽  
Cut Set ◽  
Base Band ◽  
Multi Cloud

This paper investigates the achievable per-user degrees-of-freedom (DoF) in multi-cloud based sectored hexagonal cellular networks (M-CRAN) at uplink. The network consists of N base stations (BS) and K ≤ N base band unit pools (BBUP), which function as independent cloud centers. The communication between BSs and BBUPs occurs by means of finite-capacity fronthaul links of capacities C F = μ F · 1 2 log ( 1 + P ) with P denoting transmit power. In the system model, BBUPs have limited processing capacity C BBU = μ BBU · 1 2 log ( 1 + P ) . We propose two different achievability schemes based on dividing the network into non-interfering parallelogram and hexagonal clusters, respectively. The minimum number of users in a cluster is determined by the ratio of BBUPs to BSs, r = K / N . Both of the parallelogram and hexagonal schemes are based on practically implementable beamforming and adapt the way of forming clusters to the sectorization of the cells. Proposed coding schemes improve the sum-rate over naive approaches that ignore cell sectorization, both at finite signal-to-noise ratio (SNR) and in the high-SNR limit. We derive a lower bound on per-user DoF which is a function of μ BBU , μ F , and r. We show that cut-set bound are attained for several cases, the achievability gap between lower and cut-set bounds decreases with the inverse of BBUP-BS ratio 1 r for μ F ≤ 2 M irrespective of μ BBU , and that per-user DoF achieved through hexagonal clustering can not exceed the per-user DoF of parallelogram clustering for any value of μ BBU and r as long as μ F ≤ 2 M . Since the achievability gap decreases with inverse of the BBUP-BS ratio for small and moderate fronthaul capacities, the cut-set bound is almost achieved even for small cluster sizes for this range of fronthaul capacities. For higher fronthaul capacities, the achievability gap is not always tight but decreases with processing capacity. However, the cut-set bound, e.g., at 5 M 6 , can be achieved with a moderate clustering size.

scholarly journals Modeling Binaural Unmasking of Speech Using a Blind Binaural Processing Stage

2020 ◽  
Vol 24 ◽  
pp. 233121652097563
Author(s):  
Christopher F. Hauth ◽  
Simon C. Berning ◽  
Birger Kollmeier ◽  
Thomas Brand
Keyword(s):  
Speech Recognition ◽  
Speech Intelligibility ◽  
Single Channel ◽  
Signal To Noise Ratio ◽  
Binaural Processing ◽  
Speech In Noise ◽  
Masking Level Difference ◽  
Low Pass ◽  
Speech Intelligibility Index ◽  
Filtered Speech

The equalization cancellation model is often used to predict the binaural masking level difference. Previously its application to speech in noise has required separate knowledge about the speech and noise signals to maximize the signal-to-noise ratio (SNR). Here, a novel, blind equalization cancellation model is introduced that can use the mixed signals. This approach does not require any assumptions about particular sound source directions. It uses different strategies for positive and negative SNRs, with the switching between the two steered by a blind decision stage utilizing modulation cues. The output of the model is a single-channel signal with enhanced SNR, which we analyzed using the speech intelligibility index to compare speech intelligibility predictions. In a first experiment, the model was tested on experimental data obtained in a scenario with spatially separated target and masker signals. Predicted speech recognition thresholds were in good agreement with measured speech recognition thresholds with a root mean square error less than 1 dB. A second experiment investigated signals at positive SNRs, which was achieved using time compressed and low-pass filtered speech. The results demonstrated that binaural unmasking of speech occurs at positive SNRs and that the modulation-based switching strategy can predict the experimental results.

scholarly journals Automatic spike sorting for high-density microelectrode arrays

2018 ◽  
Vol 120 (6) ◽  
pp. 3155-3171 ◽  
Author(s):  
Roland Diggelmann ◽  
Michele Fiscella ◽  
Andreas Hierlemann ◽  
Felix Franke
Keyword(s):  
Principal Component Analysis ◽  
Clustering Algorithm ◽  
State Of The Art ◽  
Microelectrode Arrays ◽  
Principal Component ◽  
Component Analysis ◽  
High Density ◽  
Sorting Algorithm ◽  
Spike Sorting ◽  
Recording Electrodes

High-density microelectrode arrays can be used to record extracellular action potentials from hundreds to thousands of neurons simultaneously. Efficient spike sorters must be developed to cope with such large data volumes. Most existing spike sorting methods for single electrodes or small multielectrodes, however, suffer from the “curse of dimensionality” and cannot be directly applied to recordings with hundreds of electrodes. This holds particularly true for the standard reference spike sorting algorithm, principal component analysis-based feature extraction, followed by k-means or expectation maximization clustering, against which most spike sorters are evaluated. We present a spike sorting algorithm that circumvents the dimensionality problem by sorting local groups of electrodes independently with classical spike sorting approaches. It is scalable to any number of recording electrodes and well suited for parallel computing. The combination of data prewhitening before the principal component analysis-based extraction and a parameter-free clustering algorithm obviated the need for parameter adjustments. We evaluated its performance using surrogate data in which we systematically varied spike amplitudes and spike rates and that were generated by inserting template spikes into the voltage traces of real recordings. In a direct comparison, our algorithm could compete with existing state-of-the-art spike sorters in terms of sensitivity and precision, while parameter adjustment or manual cluster curation was not required. NEW & NOTEWORTHY We present an automatic spike sorting algorithm that combines three strategies to scale classical spike sorting techniques for high-density microelectrode arrays: 1) splitting the recording electrodes into small groups and sorting them independently; 2) clustering a subset of spikes and classifying the rest to limit computation time; and 3) prewhitening the spike waveforms to enable the use of parameter-free clustering. Finally, we combined these strategies into an automatic spike sorter that is competitive with state-of-the-art spike sorters.

Development of a Stereotactic Radiosurgery Frame Adapter for a Multichannel MRI Coil

2020 ◽  
pp. 1-8
Author(s):  
Byungmok Kim ◽  
Yongmin Chang ◽  
Hea Jung Choi ◽  
Ki-Su Park ◽  
Ji-ung Yang ◽  
...  
Keyword(s):  
Stereotactic Radiosurgery ◽  
Single Channel ◽  
Signal To Noise Ratio ◽  
Brain Mri ◽  
Three Dimensional ◽  
Mr Images ◽  
Geometrical Accuracy ◽  
Channel Coil ◽  
T1 And T2 ◽  
Mri Coils

<b><i>Background:</i></b> The usage of multichannel brain MRI coils, which have several advantages over single-channel brain coils used for stereotactic radiosurgery (SRS), requires a frame adapter device to fit the frames inside the multichannel brain coils. However, such a frame adapter has not been developed until now. <b><i>Objective:</i></b> to develop an SRS frame adapter for multichannel MRI coils and verify the geometrical accuracy and signal-to-noise ratio (SNR) of the MR images obtained using multichannel MRI coils. <b><i>Methods:</i></b> We fabricated an SRS frame adapter for a 48-channel MRI coil using a three-dimensional (3D) printer. Furthermore, we obtained phantom and human-brain MR images with a 3.0 Tesla MRI scanner using multi- and single-channel coils. Computed tomography (CT) phantom images were also obtained as reference. We compared the coordinate errors of the multi- and single-channel coils to evaluate the geometrical accuracy. Two neurosurgeons measured the coordinates. In addition, we compared the SNR differences between multi- and single-channel coils using the T1- and T2-weighted brain images. <b><i>Results:</i></b> For the CT coordinate measurements, the correlation coefficient <i>r</i> = 1 and <i>p</i> &#x3c; 0.001 with respect to the 3 axes (Δ<i>x</i>, Δ<i>y</i>, and Δ<i>z</i>) and 3D errors (Δ<i>r</i>) showed no interpersonal differences between the 2 neurosurgeons. The results obtained using the T1-weighted images showed that a multichannel coil had smaller coordinate errors in Δ<i>x</i>, Δ<i>y</i>, Δ<i>z</i>, and Δ<i>r</i> than that observed in case of a single-channel coil (<i>p</i> &#x3c; 0.001). In case of the SNR measurements, most of the brain areas showed higher SNRs when using a multichannel coil compared with that observed when using a single-channel coil in the T1- and T2-weighted images. <b><i>Conclusion:</i></b> Compared with single-channel coils, the use of multichannel MRI coils with a newly developed frame adapter is expected to ensure successful SRS treatments with improved geometrical accuracy and SNR.

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