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A Framework for the Comparative Assessment of Neuronal Spike Sorting Algorithms towards More Accurate Off-Line and On-Line Microelectrode Arrays Data Analysis.

Regalia G, Coelli S, Biffi E, Ferrigno G, Pedrocchi A - Comput Intell Neurosci (2016)

Bottom Line: Moreover, sorting is commonly performed off-line, which is time and memory consuming and prevents researchers from having an immediate glance at ongoing experiments.The aim of this work is to provide a versatile framework to support the evaluation and comparison of different spike classification algorithms suitable for both off-line and on-line analysis.This work provides a useful and versatile instrument for a supported comparison of different options for spike sorting towards more accurate off-line and on-line MEA data analysis.

View Article: PubMed Central - PubMed

Affiliation: Neuroengineering and Medical Robotics Laboratory, Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milano, Italy.

ABSTRACT
Neuronal spike sorting algorithms are designed to retrieve neuronal network activity on a single-cell level from extracellular multiunit recordings with Microelectrode Arrays (MEAs). In typical analysis of MEA data, one spike sorting algorithm is applied indiscriminately to all electrode signals. However, this approach neglects the dependency of algorithms' performances on the neuronal signals properties at each channel, which require data-centric methods. Moreover, sorting is commonly performed off-line, which is time and memory consuming and prevents researchers from having an immediate glance at ongoing experiments. The aim of this work is to provide a versatile framework to support the evaluation and comparison of different spike classification algorithms suitable for both off-line and on-line analysis. We incorporated different spike sorting "building blocks" into a Matlab-based software, including 4 feature extraction methods, 3 feature clustering methods, and 1 template matching classifier. The framework was validated by applying different algorithms on simulated and real signals from neuronal cultures coupled to MEAs. Moreover, the system has been proven effective in running on-line analysis on a standard desktop computer, after the selection of the most suitable sorting methods. This work provides a useful and versatile instrument for a supported comparison of different options for spike sorting towards more accurate off-line and on-line MEA data analysis.

No MeSH data available.


Related in: MedlinePlus

Classification accuracy on the simulated data sets. (a) Indication of which method yielded the highest classification accuracy (CA) for each data set (marked by the red box). (b) Box-plots (median and IQR with whiskers delimited by the maximum and minimum nonoutliers values) of classification accuracy provided by all the methods on all the data sets (N = 36). The statistically significant differences are indicated as the numbers above each box-plot, the box-plot being marked with “1” referring to the method with highest CA compared to all the others and the box-plot marked with “8” referred to the method with the lowest CA compared to all the others (Friedman's test followed by Wilcoxon's matched pair test, p < 0.01).
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fig8: Classification accuracy on the simulated data sets. (a) Indication of which method yielded the highest classification accuracy (CA) for each data set (marked by the red box). (b) Box-plots (median and IQR with whiskers delimited by the maximum and minimum nonoutliers values) of classification accuracy provided by all the methods on all the data sets (N = 36). The statistically significant differences are indicated as the numbers above each box-plot, the box-plot being marked with “1” referring to the method with highest CA compared to all the others and the box-plot marked with “8” referred to the method with the lowest CA compared to all the others (Friedman's test followed by Wilcoxon's matched pair test, p < 0.01).

Mentions: The statistical analysis applied to all the possible combinations of methods confirmed the absence of a unique method outperforming the other when applied indiscriminately to all the signals. Indeed, PCA+K-means, PCA+FCM, DWT+K-means, DWT+FCM, PCA+DBC, and O-sort yielded a comparable accuracy (Figure 8). However, these performances were statistically lower compared to the utilization of the best clusterer for each signal (Figure 8(a)), as illustrated by box-plots in Figure 8(b) (box-plot on the right).


A Framework for the Comparative Assessment of Neuronal Spike Sorting Algorithms towards More Accurate Off-Line and On-Line Microelectrode Arrays Data Analysis.

Regalia G, Coelli S, Biffi E, Ferrigno G, Pedrocchi A - Comput Intell Neurosci (2016)

Classification accuracy on the simulated data sets. (a) Indication of which method yielded the highest classification accuracy (CA) for each data set (marked by the red box). (b) Box-plots (median and IQR with whiskers delimited by the maximum and minimum nonoutliers values) of classification accuracy provided by all the methods on all the data sets (N = 36). The statistically significant differences are indicated as the numbers above each box-plot, the box-plot being marked with “1” referring to the method with highest CA compared to all the others and the box-plot marked with “8” referred to the method with the lowest CA compared to all the others (Friedman's test followed by Wilcoxon's matched pair test, p < 0.01).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4863096&req=5

fig8: Classification accuracy on the simulated data sets. (a) Indication of which method yielded the highest classification accuracy (CA) for each data set (marked by the red box). (b) Box-plots (median and IQR with whiskers delimited by the maximum and minimum nonoutliers values) of classification accuracy provided by all the methods on all the data sets (N = 36). The statistically significant differences are indicated as the numbers above each box-plot, the box-plot being marked with “1” referring to the method with highest CA compared to all the others and the box-plot marked with “8” referred to the method with the lowest CA compared to all the others (Friedman's test followed by Wilcoxon's matched pair test, p < 0.01).
Mentions: The statistical analysis applied to all the possible combinations of methods confirmed the absence of a unique method outperforming the other when applied indiscriminately to all the signals. Indeed, PCA+K-means, PCA+FCM, DWT+K-means, DWT+FCM, PCA+DBC, and O-sort yielded a comparable accuracy (Figure 8). However, these performances were statistically lower compared to the utilization of the best clusterer for each signal (Figure 8(a)), as illustrated by box-plots in Figure 8(b) (box-plot on the right).

Bottom Line: Moreover, sorting is commonly performed off-line, which is time and memory consuming and prevents researchers from having an immediate glance at ongoing experiments.The aim of this work is to provide a versatile framework to support the evaluation and comparison of different spike classification algorithms suitable for both off-line and on-line analysis.This work provides a useful and versatile instrument for a supported comparison of different options for spike sorting towards more accurate off-line and on-line MEA data analysis.

View Article: PubMed Central - PubMed

Affiliation: Neuroengineering and Medical Robotics Laboratory, Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milano, Italy.

ABSTRACT
Neuronal spike sorting algorithms are designed to retrieve neuronal network activity on a single-cell level from extracellular multiunit recordings with Microelectrode Arrays (MEAs). In typical analysis of MEA data, one spike sorting algorithm is applied indiscriminately to all electrode signals. However, this approach neglects the dependency of algorithms' performances on the neuronal signals properties at each channel, which require data-centric methods. Moreover, sorting is commonly performed off-line, which is time and memory consuming and prevents researchers from having an immediate glance at ongoing experiments. The aim of this work is to provide a versatile framework to support the evaluation and comparison of different spike classification algorithms suitable for both off-line and on-line analysis. We incorporated different spike sorting "building blocks" into a Matlab-based software, including 4 feature extraction methods, 3 feature clustering methods, and 1 template matching classifier. The framework was validated by applying different algorithms on simulated and real signals from neuronal cultures coupled to MEAs. Moreover, the system has been proven effective in running on-line analysis on a standard desktop computer, after the selection of the most suitable sorting methods. This work provides a useful and versatile instrument for a supported comparison of different options for spike sorting towards more accurate off-line and on-line MEA data analysis.

No MeSH data available.


Related in: MedlinePlus