A new technique to construct a wavelet transform matching a specified signal with applications to digital, real time, spike, and overlap pattern recognition

Guido, Rodrigo Capobianco; Slaets, Jan Frans Willem; Köberle, R.; Almeida, L. O. B.; Pereira, José Carlos

doi:10.1016/j.dsp.2005.04.002

Cited by 43 publications

(15 citation statements)

References 5 publications

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“…These methods propose a signal-dependent selection of the mother wavelet based on an optimization criterion. Other techniques have used the theory of a matched filter to design a wavelet matched to a specific signal for detection purpose (Szu et al, 1992;Laterza and Olmo, 1997;Olmo et al, 2000;Chapa and Rao, 2000;Guido et al, 2006;Shark and Yu, 2006). These methods have improved event detection in comparison with methods that use non-matched wavelets.…”

Section: Introductionmentioning

confidence: 96%

Spike detection in human muscle sympathetic nerve activity using a matched wavelet approach

Salmanpour

Brown

Shoemaker

2010

Journal of Neuroscience Methods

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Section: Introductionmentioning

confidence: 96%

Spike detection in human muscle sympathetic nerve activity using a matched wavelet approach

Salmanpour

Brown

Shoemaker

2010

Journal of Neuroscience Methods

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“…Figure 11 shows two examples in which the N170 ERPs were correctly detected by our algorithm. To evaluate how well our algorithm performs, it was compared against the spikelet technique proposed by Guido et al (2006) and t-CWT proposed by Bostanov (2004). The Spikelet constructs wavelets matching a specified signal by deriving a system of linear equations from the Daubechies transform filter coefficients.…”

Section: Single Trial Eegmentioning

confidence: 99%

A wavelet based algorithm for the identification of oscillatory event-related potential components

Aniyan¹,

Philip²,

Samar

et al. 2014

Journal of Neuroscience Methods

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Event Related Potentials (ERPs) are very feeble alterations in the ongoing Electroencephalogram (EEG) and their detection is a challenging problem. Based on the unique time-based parameters derived from wavelet coefficients and the asymmetry property of wavelets a novel algorithm to separate ERP components in single-trial EEG data is described. Though illustrated as a specific application to N170 ERP detection, the algorithm is a generalized approach that can be easily adapted to isolate different kinds of ERP components. The algorithm detected the N170 ERP component with a high level of accuracy. We demonstrate that the asymmetry method is more accurate than the matching wavelet algorithm and t-CWT method by 48.67 and 8.03 percent respectively. This paper provides an off-line demonstration of the algorithm and considers issues related to the extension of the algorithm to real-time applications.

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“…It was argued that linear-filter-based spike sorting provides only moderate performance in terms of sorting quality (Wheeler and Heetderks, 1982; Lewicki, 1994; Guido et al, 2006), but it was shown more recently that this could be due to the fact that the candidate filters have been derived in the frequency domain, which was shown to be non-optimal (Vollgraf and Obermayer, 2006). …”

Section: Real-time Spike Sorting Algorithmsmentioning

confidence: 99%

High-density microelectrode array recordings and real-time spike sorting for closed-loop experiments: an emerging technology to study neural plasticity

Franke¹,

Jäckel²,

Dragaš³

et al. 2012

Front. Neural Circuits

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Understanding plasticity of neural networks is a key to comprehending their development and function. A powerful technique to study neural plasticity includes recording and control of pre- and post-synaptic neural activity, e.g., by using simultaneous intracellular recording and stimulation of several neurons. Intracellular recording is, however, a demanding technique and has its limitations in that only a small number of neurons can be stimulated and recorded from at the same time. Extracellular techniques offer the possibility to simultaneously record from larger numbers of neurons with relative ease, at the expenses of increased efforts to sort out single neuronal activities from the recorded mixture, which is a time consuming and error prone step, referred to as spike sorting. In this mini-review, we describe recent technological developments in two separate fields, namely CMOS-based high-density microelectrode arrays, which also allow for extracellular stimulation of neurons, and real-time spike sorting. We argue that these techniques, when combined, will provide a powerful tool to study plasticity in neural networks consisting of several thousand neurons in vitro.

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A new technique to construct a wavelet transform matching a specified signal with applications to digital, real time, spike, and overlap pattern recognition

Cited by 43 publications

References 5 publications

Spike detection in human muscle sympathetic nerve activity using a matched wavelet approach

Spike detection in human muscle sympathetic nerve activity using a matched wavelet approach

A wavelet based algorithm for the identification of oscillatory event-related potential components

High-density microelectrode array recordings and real-time spike sorting for closed-loop experiments: an emerging technology to study neural plasticity

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