Validation in Principal Components Analysis Applied to EEG Data
Joint Authors
Costa, João Carlos G. D.
Da-Silva, Paulo José G.
Almeida, Renan Moritz V. R.
Infantosi, Antonio Fernando Catelli
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-09-07
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
The well-known multivariate technique Principal Components Analysis (PCA) is usually applied to a sample, and so component scores are subjected to sampling variability.
However, few studies address their stability, an important topic when the sample size is small.
This work presents three validation procedures applied to PCA, based on confidence regions generated by a variant of a nonparametric bootstrap called the partial bootstrap: (i) the assessment of PC scores variability by the spread and overlapping of “confidence regions” plotted around these scores; (ii) the use of the confidence regions centroids as a validation set; and (iii) the definition of the number of nontrivial axes to be retained for analysis.
The methods were applied to EEG data collected during a postural control protocol with twenty-four volunteers.
Two axes were retained for analysis, with 91.6% of explained variance.
Results showed that the area of the confidence regions provided useful insights on the variability of scores and suggested that some subjects were not distinguishable from others, which was not evident from the principal planes.
In addition, potential outliers, initially suggested by an analysis of the first principal plane, could not be confirmed by the confidence regions.
American Psychological Association (APA)
Costa, João Carlos G. D.& Da-Silva, Paulo José G.& Almeida, Renan Moritz V. R.& Infantosi, Antonio Fernando Catelli. 2014. Validation in Principal Components Analysis Applied to EEG Data. Computational and Mathematical Methods in Medicine،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1034664
Modern Language Association (MLA)
Costa, João Carlos G. D.…[et al.]. Validation in Principal Components Analysis Applied to EEG Data. Computational and Mathematical Methods in Medicine No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-1034664
American Medical Association (AMA)
Costa, João Carlos G. D.& Da-Silva, Paulo José G.& Almeida, Renan Moritz V. R.& Infantosi, Antonio Fernando Catelli. Validation in Principal Components Analysis Applied to EEG Data. Computational and Mathematical Methods in Medicine. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1034664
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1034664