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.

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 SpikeDeep-Classifier: A deep-learning based fully automatic offline spike sorting algorithm

2020 ◽  
Author(s):  
Muhammad Saif-ur-Rehman ◽  
Omair Ali ◽  
Susanne Dyck ◽  
Robin Lienkämper ◽  
Marita Metzler ◽  
...  
Keyword(s):  
Deep Learning ◽  
Sorting Algorithm ◽  
Spike Sorting ◽  
Fully Automatic

scholarly journals Resolution of Spike Overlapping by Biogeography-Based Optimization

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1469
Author(s):  
Giovanni Chiarion ◽  
Luca Mesin
Keyword(s):  
Electrical Impedance ◽  
Single Channel ◽  
Specific Problem ◽  
Simulated Data ◽  
True Positive Rate ◽  
Spike Sorting ◽  
Common Basis ◽  
Performance Indexes ◽  
Positive Rate ◽  
Different Sources

There are many cases in which the separation of different sources from single channel recordings is important, for example, in fluorescence spectral overlap compensation, electrical impedance signaling, intramuscular electromyogram decomposition or in the case of spike sorting of neuron potentials from microelectrode arrays (MEA). Focusing on the latter, the problem can be faced by identifying spikes emerging from the background and clustering into different groups, indicating the activity of different neurons. Problems are found when more spikes are superimposed in overlapped waveforms. We discuss the application of Biogeography-Based Optimization (BBO) to resolve this specific problem. Our algorithm is compared with three spike-sorting methods (SpyKING Circus, Common Basis Pursuit and Klusta), showing statistically better performance (in terms of F1 score, True Positive Rate—TPR and Positive Predictive Value—PPV) in resolving overlaps in realistic, simulated data. Specifically, BBO showed median F1, TPR and PPV of 100%, 100% and about 75%, respectively, considering a simulated noise with the same spectral density as the experimental one and a similar power with highly statistically significant improvements of at least two performance indexes over each of the other three tested algorithms.

Ion-channel gating mechanisms: model identification and parameter estimation from single channel recordings

1989 ◽  
Vol 236 (1285) ◽  
pp. 385-416 ◽  
Keyword(s):  
Parameter Estimation ◽  
Single Channel ◽  
Channel Gating ◽  
Model Identification ◽  
Simulated Data ◽  
Process Models ◽  
Gating Mechanisms ◽  
Gating Mechanism ◽  
Incorrect Model ◽  
Schwarz Criterion

Patch-clamp data may be analysed in terms of Markov process models of channel gating mechanisms. We present a maximum likelihood algorithm for estimation of gating parameters from records where only a single channel is present. Computer simulated data for three different models of agonist receptor gated channels are used to demonstrate the performance of the procedure. Full details of the implementation of the algorithm are given for an example gating mechanism. The effects of omission of brief openings and closings from the single-channel data on parameter estimation are explored. A strategy for discriminating between alternative possible gating models, based upon use of the Schwarz criterion, is described. Omission of brief events is shown not to lead to incorrect model identification, except in extreme circumstances. Finally, the algorithm is extended to include channel gating models exhibiting multiple conductance levels.

scholarly journals Constructing an Emotion Estimation Model Based on EEG/HRV Indexes Using Feature Extraction and Feature Selection Algorithms

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2910
Author(s):  
Kei Suzuki ◽  
Tipporn Laohakangvalvit ◽  
Ryota Matsubara ◽  
Midori Sugaya
Keyword(s):  
Feature Selection ◽  
Single Channel ◽  
Feature Selection Method ◽  
Classification Model ◽  
Features Selection ◽  
Selection Methods ◽  
Emotion Classification ◽  
Model Based ◽  
Physiological Indexes ◽  
Emotion Estimation

In human emotion estimation using an electroencephalogram (EEG) and heart rate variability (HRV), there are two main issues as far as we know. The first is that measurement devices for physiological signals are expensive and not easy to wear. The second is that unnecessary physiological indexes have not been removed, which is likely to decrease the accuracy of machine learning models. In this study, we used single-channel EEG sensor and photoplethysmography (PPG) sensor, which are inexpensive and easy to wear. We collected data from 25 participants (18 males and 7 females) and used a deep learning algorithm to construct an emotion classification model based on Arousal–Valence space using several feature combinations obtained from physiological indexes selected based on our criteria including our proposed feature selection methods. We then performed accuracy verification, applying a stratified 10-fold cross-validation method to the constructed models. The results showed that model accuracies are as high as 90% to 99% by applying the features selection methods we proposed, which suggests that a small number of physiological indexes, even from inexpensive sensors, can be used to construct an accurate emotion classification model if an appropriate feature selection method is applied. Our research results contribute to the improvement of an emotion classification model with a higher accuracy, less cost, and that is less time consuming, which has the potential to be further applied to various areas of applications.

scholarly journals Coordinate based meta-analysis of networks in neuroimaging studies

2018 ◽  
Author(s):  
CR Tench ◽  
Radu Tanasescu ◽  
CS Constantinescu ◽  
DP Auer ◽  
WJ Cottam
Keyword(s):  
Spatial Clustering ◽  
Meta Analysis ◽  
Simulated Data ◽  
Real Data ◽  
Network Effect ◽  
Effect Sizes ◽  
Multivariate Normal ◽  
Z Scores ◽  
Independent Effects ◽  
Multiple Clusters

AbstractMeta-analysis of published neuroimaging results is commonly performed using coordinate based meta-analysis (CBMA). Most commonly CBMA algorithms detect spatial clustering of reported coordinates across multiple studies by assuming that results relating to the common hypothesis fall in similar anatomical locations. The null hypothesis is that studies report uncorrelated results, which is simulated by random coordinates. It is assumed that multiple clusters are independent yet it is likely that multiple results reported per study are not, and in fact represent a network effect. Here the multiple reported effect sizes (reported peak Z scores) are assumed multivariate normal, and maximum likelihood used to estimate the parameters of the covariance matrix. The hypothesis is that the effect sizes are correlated. The parameters are covariance of effect size, considered as edges of a network, while clusters are considered as nodes. In this way coordinate based meta-analysis of networks (CBMAN) estimates a network of reported meta-effects, rather than multiple independent effects (clusters).CBMAN uses only the same data as CBMA, yet produces extra information in terms of the correlation between clusters. Here it is validated on numerically simulated data, and demonstrated on real data used previously to demonstrate CBMA. The CBMA and CBMAN clusters are similar, despite the very different hypothesis.

scholarly journals Ring artifact reduction via multiscale nonlocal collaborative filtering of spatially correlated noise

2021 ◽  
Vol 28 (3) ◽  
Author(s):  
Ymir Mäkinen ◽  
Stefano Marchesini ◽  
Alessandro Foi
Keyword(s):  
Multiple Scales ◽  
Simulated Data ◽  
Block Matching ◽  
Artifact Reduction ◽  
Correlated Noise ◽  
Spatially Correlated ◽  
Ring Artifact ◽  
Fully Automatic ◽  
Micro Tomography ◽  
Spatially Correlated Noise

X-ray micro-tomography systems often suffer severe ring artifacts in reconstructed images. These artifacts are caused by defects in the detector, calibration errors, and fluctuations producing streak noise in the raw sinogram data. In this work, these streaks are modeled in the sinogram domain as additive stationary correlated noise upon logarithmic transformation. Based on this model, a streak removal procedure is proposed where the Block-Matching and 3-D (BM3D) filtering algorithm is applied across multiple scales, achieving state-of-the-art performance in both real and simulated data. Specifically, the proposed fully automatic procedure allows for attenuation of streak noise and the corresponding ring artifacts without creating major distortions common to other streak removal algorithms.

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.

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