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Multivariate assessment of event-related potentials with the t-CWT method
BACKGROUND: 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...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4635610/ https://www.ncbi.nlm.nih.gov/pubmed/26541673 http://dx.doi.org/10.1186/s12868-015-0185-z |
Sumario: | BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12868-015-0185-z) contains supplementary material, which is available to authorized users. |
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