The M-Sorter: An automatic and robust spike detection and classification system

Yuan, Yuan; Yang, Chenhui; Si, Jennie

doi:10.1016/j.jneumeth.2012.07.012

Cited by 44 publications

(38 citation statements)

References 28 publications

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“…Offline spike detection and sorting was performed using the M-Sorter from our own laboratory (Yuan et al 2012). The sorter utilizes the multiscale correlation of wavelet coefficients (MCWC) for spike detection (Yang et al 2011).…”

Section: Methodsmentioning

confidence: 99%

Cortical neural responses to previous trial outcome during learning of a directional choice task

Yuan

Mao

2015

Journal of Neurophysiology

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Section: Methodsmentioning

confidence: 99%

Cortical neural responses to previous trial outcome during learning of a directional choice task

Yuan

Mao

2015

Journal of Neurophysiology

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“…Auto-covariance sequences, r i (k), were then calculated at lags k ∈ [−29, 29] for each window and each sensor i ∈ [1,4]. The sequences were averaged across all 375 noise windows, and used to generate each Σ i,i .…”

Section: Parameter Estimationmentioning

confidence: 99%

“…The template is then usually generated from extracellular action potentials (EAPs) selected by an analyst from the data [3], [6]. Un-supervised algorithms using wavelets as templates, and assuming no prior knowledge about the signal, are also becoming increasingly popular [4], [5], [7], [8].…”

Section: Introductionmentioning

confidence: 99%

A supervised multi-sensor matched filter for the detection of extracellular action potentials

Szymanska¹,

Doty²,

Scannell³

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-Multi-sensor extracellular recording takes advantage of several electrode channels to record from multiple neurons at the same time. However, the resulting low signal-tonoise ratio (SNR) combined with biological noise makes signal detection, the first step of any neurophysiological data analysis, difficult. A matched filter was therefore designed to better detect extracellular action potentials (EAPs) from multi-sensor extracellular recordings. The detector was tested on tetrode data from a locust antennal lobe and assessed against three trained analysts. 25 EAPs and noise samples were selected manually from the data and used for training. To reduce complexity, the filter assumed that the underlying noise in the data was spatially white. The detector performed with an average TP and FP rate of 84.62% and 16.63% respectively. This high level of performance indicates the algorithm is suitable for widespread use.

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“…The clustering can be performed in a variety of feature spaces spanned by features such as peak or valley amplitude, principal components, or wavelet coefficients. In addition to these commercial or widely used tools, new algorithms for performing spike sorting continue to be developed [5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Taller Peaks: an improved spike detection algorithm that simultaneously reduces type I and type II errors for Wave{\_}clus

Okatan¹

2017

Turk J Elec Eng & Comp Sci

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Abstract:Wave clus is an unsupervised spike detection and sorting algorithm that has been used in dozens of experimental studies as a spike sorting tool. It is often used as a benchmark for comparing the performance of new spike sorting algorithms. For these reasons, the spike detection performance of Wave clus is important for both experimental and computational studies that involve spike sorting. Two measures of spike detection performance are the number of false positive detections (type I error) and the number of missed spikes (type II error). Here, a new spike detection algorithm is proposed that reduces the number of misses and false positives of Wave clus in a widely used simulated data set across the entire range of commonly used detection thresholds. The algorithm accepts a spike if its amplitude is larger than the amplitude of its two immediate neighbors, where an immediate neighbor is the nearest peak of the same polarity within ± 1 refractory period. The simultaneous reduction that is achieved in the number of false positives and misses is important for experimental and computational studies that use Wave clus as a spike sorting tool or as a benchmark. A software patch that incorporates the algorithm into Wave clus as an optional spike detection algorithm is provided.

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The M-Sorter: An automatic and robust spike detection and classification system

Cited by 44 publications

References 28 publications

Cortical neural responses to previous trial outcome during learning of a directional choice task

Cortical neural responses to previous trial outcome during learning of a directional choice task

A supervised multi-sensor matched filter for the detection of extracellular action potentials

Taller Peaks: an improved spike detection algorithm that simultaneously reduces type I and type II errors for Wave{\_}clus

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