Performance evaluation of PCA-based spike sorting algorithms

Adamos, Dimitrios A.; Kosmidis, Efstratios K.; Theophilidis, George

doi:10.1016/j.cmpb.2008.04.011

Cited by 98 publications

(80 citation statements)

References 32 publications

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“…In theory, therefore, background noise in recordings made from hook or suction electrodes of large myelinated nerves could be approximated by a Gaussian noise process. In practice, however, ephaptic interactions between axons and crosstalk between amplifiers and recording equipment introduce various levels of correlation [13]. Furthermore any interference from other sources such as muscle activity will appear with some level of correlation.…”

Section: B Noise Modelsmentioning

confidence: 99%

“…In practice, however, various interactions between axons and crosstalk between amplifiers and recording equipment introduce some correlation [13]. Therefore a correlated noise model was also considered in the simulation study, based on the Ornstein-Uhlenbeck process [22].…”

Section: B Comparison Of Awgn and Oumentioning

confidence: 99%

“…It is a first-order analytical model of the extracellular AP and is based on a damped sinusoidal function as proposed in [13], [20].…”

Section: A Single Fiber Action Potential Modelmentioning

confidence: 99%

“…(there are more advanced statistical methods such a Hartigan's dip test [11], [12]). In PCA it is common to reduce the number of dimensions considered for clustering by simply truncating to N dimensions, where N is typically 2 or 3 [13].…”

Section: Introductionmentioning

confidence: 99%

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A New Method for Neural Spike Alignment: The Centroid Filter

Metcalfe

Clarke

Donaldson

et al. 2017

IEEE Trans. Neural Syst. Rehabil. Eng.

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Highlights A key problem in neural spike sorting is highlighted.  A new method to measure the position of action potentials in real-time is described.  This method is validated using detailed simulations. Major improvements are demonstrated compared to standard methods. AbstractBackground: This paper describes a new method to calculate the positions of action potentials (APs) in extracellular neural recordings in real-time. Extracellular recordings made using various types of electrode structures are a key tool in experimental electrophysiology and involve the identification of signals from individual neurons, a process known by the generic name spike sorting. An essential feature of the process is the alignment of individual APs ('spikes') in time. Given the recordings are invariably made in noisy environments there is a tendency to timing jitter leading to alignment errors. This is especially true if single point methods of AP location are employed. New Method: The problems of single point AP location in the presence of noise are significantly reduced by the use of the centroid as a measure of position, which is based on a time average of all the points in the AP. The method is described and analysed in detail and it is shown that a very economical hardware realisation is possible. Results:The new methods are investigated by simulation using deterministic models of nerve signals (i.e. APs with added noise; both correlated and uncorrelated noise models are considered) as well as results measured from a hardware implementation. A power efficient realisation of the centroid filter is demonstrated using a complex programmable logic device (CPLD). Comparison with Existing Method:Detailed simulations demonstrate the superiority of the centroid method compared to several standard, single point metrics. Conclusions: A new method has been suggested that significantly improves one of the key issues in neural spike sorting. The technique has the potential to influence significantly the design of electrophysiological recording systems in the future.

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Section: B Noise Modelsmentioning

confidence: 99%

Section: B Comparison Of Awgn and Oumentioning

confidence: 99%

“…It is a first-order analytical model of the extracellular AP and is based on a damped sinusoidal function as proposed in [13], [20].…”

Section: A Single Fiber Action Potential Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Method for Neural Spike Alignment: The Centroid Filter

Metcalfe

Clarke

Donaldson

et al. 2017

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Then, a feature extraction step characterises detected spikes, the main property looked for among these features being that they present a multimodal distribution that ideally allows to separate spikes fired by different neurons. Principal Component Analysis (PCA) and wavelet decomposition have widely been used in the literature for feature extraction [3][4][5][6]. To end, and based on these features, a clustering step is necessary to relate each spike to a particular neuron.…”

Section: Spike Sortingmentioning

confidence: 99%

Spike sorting at sub-Nyquist rates

Caballero

Urigüen

Schultz

et al. 2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Spike sorting relies on the ability to establish the temporal occurrence of action potentials and their relation to specific neurons. Neural information is intrinsically compressible and as such suitable for sparse sampling. Potentially, this should allow for the use of multi-channel recordings, which is particularly advantageous to improve spike sorting.In this paper we propose a novel algorithm capable of sampling neural data at sub-Nyquist rates, yielding the same performance for spike sorting as traditional schemes.

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Skeletal myotube integration with planar microelectrode arrays in vitro for spatially selective recording and stimulation: A comparison of neuronal and myotube extracellular action potentials

Langhammer

Kutzing

Luo

et al. 2011

Biotechnology Progress

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Microelectrode array (MEA) technology holds tremendous potential in the fields of bio-detection, lab-on-a-chip applications, and tissue engineering by facilitating non-invasive electrical interaction with cells in vitro. To date, significant efforts at integrating the cellular component with this detection technology have worked exclusively with neurons or cardiac myocytes. We investigate the feasibility of using MEAs to record from skeletal myotubes derived from primary myoblasts as a way of introducing a third electrogenic cell type and expanding the potential end applications for MEA-based biosensors. We find that the extracellular action potentials (EAPs) produced by spontaneously contractile myotubes have similar amplitudes to neuronal EAPs. It is possible to classify myotube EAPs by biological signal source using a shape-based spike sorting process similar to that used to analyze neural spike trains. Successful spike-sorting is indicated by a low within-unit variability of myotube EAPs. Additionally, myotube activity can cause simultaneous activation of multiple electrodes, in a similar fashion to the activation of electrodes by networks of neurons. The existence of multiple electrode activation patterns indicates the presence of several large, independent myotubes. The ability to identify these patterns suggests that MEAs may provide an electrophysiological basis for examining the process by which myotube independence is maintained despite rapid myoblast fusion during differentiation. Finally, it is possible to use the underlying electrodes to selectively stimulate individual myotubes without stimulating others nearby. Potential uses of skeletal myotubes grown on MEA substrates include lab-on-a-chip applications, tissue engineering, co-cultures with motor neurons, and neural interfaces.

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Performance evaluation of PCA-based spike sorting algorithms

Cited by 98 publications

References 32 publications

A New Method for Neural Spike Alignment: The Centroid Filter

A New Method for Neural Spike Alignment: The Centroid Filter

Spike sorting at sub-Nyquist rates

Skeletal myotube integration with planar microelectrode arrays in vitro for spatially selective recording and stimulation: A comparison of neuronal and myotube extracellular action potentials

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