Feature extraction using first and second derivative extrema (FSDE) for real-time and hardware-efficient spike sorting

Paraskevopoulou, Sivylla E.; Barsakcioglu, Deren Y.; Saberi, Mohammed R.; Eftekhar, Amir; Constandinou, Timothy G.

doi:10.1016/j.jneumeth.2013.01.012

Cited by 82 publications

(69 citation statements)

References 37 publications

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“…In (Paraskevopoulou et al, 2013), we proposed a hardware-implementable (high accuracy and low computational complexity) feature extraction method that uses derivative-based features. The derivatives are computed as the difference between the current and previous sample points of the spike waveform (s), according to Eq.…”

Section: Fsdementioning

confidence: 99%

“…However, the most significant information is contained in the first few. The main disadvantage of the method is its significant computational complexity (Gibson et al, 2008;Paraskevopoulou et al, 2013). …”

Section: Principal Component Analysismentioning

confidence: 99%

“…In these tests, FSDE is used for feature extraction (as described in (Paraskevopoulou et al, 2013)) with N f eatures =3(=F).…”

Section: Ham Clustering On Simulated Neural Signalsmentioning

confidence: 99%

“…These reduction methods can simply be in the form of data compression but several works have demonstrated hardware implementable feature extraction methods. These includes those based on derivative (Gibson et al, 2008;Paraskevopoulou et al, 2013), templates (Rizk et al, 2009), zero crossings (Kamboh and Mason, 2012) and neuronal spike shape and area (Zviagintsev et al, 2005), to name a few.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical Adaptive Means (HAM) clustering for hardware-efficient, unsupervised and real-time spike sorting

Paraskevopoulou

Eftekhar

et al. 2014

Journal of Neuroscience Methods

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This work presents a novel unsupervised algorithm for real-time adaptive clustering of neural spike data (spike sorting). The proposed Hierarchical Adaptive Means (HAM) clustering method combines centroid-based clustering with hierarchical cluster connectivity to classify incoming spikes using groups of clusters. It is described how the proposed method can adaptively track the incoming spike data without requiring any past history, iteration or training and autonomously determines the number of spike classes. Its performance (classification accuracy) has been tested using multiple datasets (both simulated and recorded) achieving a near-identical accuracy compared to k-means (using 10-iterations and provided with the number of spike classes). Also, its robustness in applying to different feature extraction methods has been demonstrated by achieving classification accuracies above 80% across multiple datasets. Last but crucially, its low complexity, that has been quantified through both memory and computation requirements makes this method hugely attractive for future hardware implementation.

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Section: Fsdementioning

confidence: 99%

Section: Principal Component Analysismentioning

confidence: 99%

“…In these tests, FSDE is used for feature extraction (as described in (Paraskevopoulou et al, 2013)) with N f eatures =3(=F).…”

Section: Ham Clustering On Simulated Neural Signalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hierarchical Adaptive Means (HAM) clustering for hardware-efficient, unsupervised and real-time spike sorting

Paraskevopoulou

Eftekhar

et al. 2014

Journal of Neuroscience Methods

Self Cite

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“…e derivatives have been used to smoothen the signals by filtering the noise, for peak detection, trace analysis, and feature extraction, among others [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A Computational Fractional Signal Derivative Method

Salinas

Salas

Mellado

et al. 2018

Modelling and Simulation in Engineering

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We propose an efficient computational method to obtain the fractional derivative of a digital signal. e proposal consists of a new interpretation of the Grünwald-Letnikov differintegral operator where we have introduced a finite Cauchy convolution with the Grünwald-Letnikov dynamic kernel. e method can be applied to any signal without knowing its analytical form. In the experiments, we have compared the proposed Grünwald-Letnikov computational fractional derivative method with the Riemman-Louville fractional derivative approach for two well-known functions. e simulations exhibit similar results for both methods; however, the Grünwald-Letnikov method outperforms the other approach in execution time. Finally, we show an application of how our proposal can be useful to find the fractional relationship between two well-known biomedical signals.

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German Bight storm activity, 1897–2018

Krieger

Krueger²,

Feser³

et al. 2020

Intl Journal of Climatology

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This study investigates the evolution of German Bight (southeastern North Sea) storminess from 1897 to 2018 through analysing upper quantiles of geostrophic wind speeds, which act as a proxy for past storm activity. Here, geostrophic wind speeds are calculated from triplets of mean sea level pressure observations that form triangles over the German Bight. The data used in the manuscript are provided by the International Surface Pressure Databank and the national meteorological services of Denmark, Germany, and the Netherlands. The derivation of storm activity is achieved by enhancing the established triangle proxy method via combining and merging storminess time series from numerous partially overlapping triangles in an ensemble-like manner. The utilized approach allows for the construction of robust, long-term and subdaily German Bight storminess time series. Further, the method provides insights into the underlying uncertainty of the time series. The results show that storm activity over the German Bight is subject to multidecadal variability. The latest decades are characterized by an increase in activity from the 1960s to the 1990s, followed by a decline lasting into the 2000s and below-average activity up until present. The results are backed through a comparison with reanalysis products from four datasets, which provide high-resolution wind and pressure data starting in 1979 and offshore wind speed measurements taken from the FINO-WIND project. This study also finds that German Bight storminess positively correlates with storminess in the NE Atlantic in general. In certain years, however, notably different levels of storm activity in the two regions can be found, which likely result from shifted large-scale circulation patterns.

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Feature extraction using first and second derivative extrema (FSDE) for real-time and hardware-efficient spike sorting

Cited by 82 publications

References 37 publications

Hierarchical Adaptive Means (HAM) clustering for hardware-efficient, unsupervised and real-time spike sorting

Hierarchical Adaptive Means (HAM) clustering for hardware-efficient, unsupervised and real-time spike sorting

A Computational Fractional Signal Derivative Method

German Bight storm activity, 1897–2018

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