An exploratory data analysis of electroencephalograms using the functional boxplots approach

Handle URI:
http://hdl.handle.net/10754/575527
Title:
An exploratory data analysis of electroencephalograms using the functional boxplots approach
Authors:
Ngo, Duy; Sun, Ying ( 0000-0001-6703-4270 ) ; Genton, Marc G. ( 0000-0001-6467-2998 ) ; Wu, Jennifer; Srinivasan, Ramesh; Cramer, Steven C.; Ombao, Hernando
Abstract:
Many model-based methods have been developed over the last several decades for analysis of electroencephalograms (EEGs) in order to understand electrical neural data. In this work, we propose to use the functional boxplot (FBP) to analyze log periodograms of EEG time series data in the spectral domain. The functional bloxplot approach produces a median curve—which is not equivalent to connecting medians obtained from frequency-specific boxplots. In addition, this approach identifies a functional median, summarizes variability, and detects potential outliers. By extending FBPs analysis from one-dimensional curves to surfaces, surface boxplots are also used to explore the variation of the spectral power for the alpha (8–12 Hz) and beta (16–32 Hz) frequency bands across the brain cortical surface. By using rank-based nonparametric tests, we also investigate the stationarity of EEG traces across an exam acquired during resting-state by comparing the spectrum during the early vs. late phases of a single resting-state EEG exam.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
An exploratory data analysis of electroencephalograms using the functional boxplots approach 2015, 9 Frontiers in Neuroscience
Publisher:
Frontiers Media SA
Journal:
Frontiers in Neuroscience
Issue Date:
19-Aug-2015
DOI:
10.3389/fnins.2015.00282
Type:
Article
ISSN:
1662-453X
Additional Links:
http://journal.frontiersin.org/article/10.3389/fnins.2015.00282
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorNgo, Duyen
dc.contributor.authorSun, Yingen
dc.contributor.authorGenton, Marc G.en
dc.contributor.authorWu, Jenniferen
dc.contributor.authorSrinivasan, Rameshen
dc.contributor.authorCramer, Steven C.en
dc.contributor.authorOmbao, Hernandoen
dc.date.accessioned2015-08-23T15:08:02Zen
dc.date.available2015-08-23T15:08:02Zen
dc.date.issued2015-08-19en
dc.identifier.citationAn exploratory data analysis of electroencephalograms using the functional boxplots approach 2015, 9 Frontiers in Neuroscienceen
dc.identifier.issn1662-453Xen
dc.identifier.doi10.3389/fnins.2015.00282en
dc.identifier.urihttp://hdl.handle.net/10754/575527en
dc.description.abstractMany model-based methods have been developed over the last several decades for analysis of electroencephalograms (EEGs) in order to understand electrical neural data. In this work, we propose to use the functional boxplot (FBP) to analyze log periodograms of EEG time series data in the spectral domain. The functional bloxplot approach produces a median curve—which is not equivalent to connecting medians obtained from frequency-specific boxplots. In addition, this approach identifies a functional median, summarizes variability, and detects potential outliers. By extending FBPs analysis from one-dimensional curves to surfaces, surface boxplots are also used to explore the variation of the spectral power for the alpha (8–12 Hz) and beta (16–32 Hz) frequency bands across the brain cortical surface. By using rank-based nonparametric tests, we also investigate the stationarity of EEG traces across an exam acquired during resting-state by comparing the spectrum during the early vs. late phases of a single resting-state EEG exam.en
dc.language.isoenen
dc.publisherFrontiers Media SAen
dc.relation.urlhttp://journal.frontiersin.org/article/10.3389/fnins.2015.00282en
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.http://creativecommons.org/licenses/by/4.0/en
dc.subjectEEGs time seriesen
dc.subjectfunctional boxplotsen
dc.subjectsurface boxplotsen
dc.subjectspectral analysisen
dc.subjectband depthen
dc.subjectexploratory analysisen
dc.subjectstationarityen
dc.titleAn exploratory data analysis of electroencephalograms using the functional boxplots approachen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalFrontiers in Neuroscienceen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDepartment of Statistics, University of California, Irvine, Irvine, CA, USAen
dc.contributor.institutionDepartment of Anatomy & Neurobiology, University of California, Irvine, Irvine, CA, USAen
dc.contributor.institutionDepartment of Cognitive Sciences, University of California, Irvine, Irvine, CA, USAen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorSun, Yingen
kaust.authorGenton, Marc G.en
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