Joint-IVA for identification of discriminating features in EEG: Application to a driving study

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dc.contributor.authorGabrielson, Ben
dc.contributor.authorAkhonda, M.A.B.S.
dc.contributor.authorBhinge, Suchita
dc.contributor.authorBrooks, Justin
dc.contributor.authorLong, Qunfang
dc.contributor.authorAdali, Tulay
dc.date.accessioned2020-09-10T17:10:11Z
dc.date.available2020-09-10T17:10:11Z
dc.date.issued2020-08-01
dc.description.abstractWe propose a new method, joint independent vector analysis (jIVA), for obtaining discriminating features, i.e., interpretable signatures from medical data that can be used to study differences between multiple conditions or groups. The method is especially attractive for event related studies of electroencephalogram (EEG) data as it enables one to take advantage of the cross information across multiple channels effectively while enabling the use of information from multiple epochs. We introduce the general model and then demonstrate its successful application to EEG data collected during a driving experiment. As opposed to traditional analysis techniques that only detect differences, we identify statistically significant differences in measured band power showing when and how the differences occur for two experimental conditions across the same group of subjects. We compare jIVA features to those produced from competing data-driven approaches and demonstrate the advantages of jIVA as it fully leverages the statistical dependencies across multiple electrodes, and note its promise as a powerful data-driven method of obtaining informative features of multiset data.en_US
dc.description.urihttps://www.sciencedirect.com/science/article/abs/pii/S174680942030104X#!en_US
dc.format.extent11 pagesen_US
dc.genrejournal articles postprintsen_US
dc.identifierdoi:10.13016/m22p96-jf0r
dc.identifier.citationGabrielson, Ben; Akhonda, M.A.B.S.; Bhinge, Suchita; Brooks, Justin; Long, Qunfang; Adali, Tülay; Joint-IVA for identification of discriminating features in EEG: Application to a driving study; Biomedical Signal Processing and Control,Volume 61, August 2020; https://www.sciencedirect.com/science/article/abs/pii/S174680942030104X#!en_US
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2020.101948
dc.identifier.urihttp://hdl.handle.net/11603/19632
dc.language.isoen_USen_US
dc.publisherElsevieren_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)*
dc.rightsAccess to this item will begin on 2022-08-01
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectUMBC Machine Learning for Signal Processing Laben_US
dc.titleJoint-IVA for identification of discriminating features in EEG: Application to a driving studyen_US
dc.typeTexten_US

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