Authors:
Shengkun Xie
1
and
Anna Lawniczak
2
Affiliations:
1
Ted Rogers School of Management, Ryerson University, Toronto and Canada
;
2
Department of Mathematics and Statistics, University of Guelph, Guelph and Canada
Keyword(s):
Functional Data Analysis, Power Spectrum, Functional Principal Component Analysis, EEG, Epilepsy Diagnosis.
Related
Ontology
Subjects/Areas/Topics:
Feature Selection and Extraction
;
Pattern Recognition
;
Theory and Methods
Abstract:
Functional data analysis is a natural tool for functional data to discover functional patterns. It is also often used to investigate the functional variation of random signals. In this work, we propose a novel approach by analyzing EEG signals in the spectral domain using functional data analysis techniques including functional descriptive statistics, functional probes, and functional principal component analysis. By first transforming EEG signals into their power spectra, the functionality of random signals is greatly enhanced. Because of this improvement, the application of functional data analysis becomes meaningful in feature extraction of random signals. Our study also illustrates a great potential of using functional PCA as a feature extractor for EEG signals in epilepsy diagnosis.