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Multivariate assessment of event-related potentials with the t-CWT method

Overview of attention for article published in BMC Neuroscience, November 2015
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Title
Multivariate assessment of event-related potentials with the t-CWT method
Published in
BMC Neuroscience, November 2015
DOI 10.1186/s12868-015-0185-z
Pubmed ID
Authors

Vladimir Bostanov

Abstract

Event-related brain potentials (ERPs) are usually assessed with univariate statistical tests although they are essentially multivariate objects. Brain-computer interface applications are a notable exception to this practice, because they are based on multivariate classification of single-trial ERPs. Multivariate ERP assessment can be facilitated by feature extraction methods. One such method is t-CWT, a mathematical-statistical algorithm based on the continuous wavelet transform (CWT) and Student's t-test. This article begins with a geometric primer on some basic concepts of multivariate statistics as applied to ERP assessment in general and to the t-CWT method in particular. Further, it presents for the first time a detailed, step-by-step, formal mathematical description of the t-CWT algorithm. A new multivariate outlier rejection procedure based on principal component analysis in the frequency domain is presented as an important pre-processing step. The MATLAB and GNU Octave implementation of t-CWT is also made publicly available for the first time as free and open source code. The method is demonstrated on some example ERP data obtained in a passive oddball paradigm. Finally, some conceptually novel applications of the multivariate approach in general and of the t-CWT method in particular are suggested and discussed. Hopefully, the publication of both the t-CWT source code and its underlying mathematical algorithm along with a didactic geometric introduction to some basic concepts of multivariate statistics would make t-CWT more accessible to both users and developers in the field of neuroscience research.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 21 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 19%
Other 2 10%
Student > Bachelor 2 10%
Researcher 2 10%
Student > Ph. D. Student 1 5%
Other 3 14%
Unknown 7 33%
Readers by discipline Count As %
Engineering 3 14%
Neuroscience 3 14%
Medicine and Dentistry 2 10%
Psychology 2 10%
Decision Sciences 1 5%
Other 3 14%
Unknown 7 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 November 2015.
All research outputs
#20,295,501
of 22,832,057 outputs
Outputs from BMC Neuroscience
#1,055
of 1,245 outputs
Outputs of similar age
#239,156
of 285,414 outputs
Outputs of similar age from BMC Neuroscience
#33
of 43 outputs
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