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Article: Taking into account latency, amplitude, and morphology: Improved estimation of single-trial ERPs by wavelet filtering and multiple linear regression

TitleTaking into account latency, amplitude, and morphology: Improved estimation of single-trial ERPs by wavelet filtering and multiple linear regression
Authors
Issue Date2011
Citation
Journal Of Neurophysiology, 2011, v. 106 n. 6, p. 3216-3229 How to Cite?
AbstractAcross-trial averaging is a widely used approach to enhance the signal-to-noise ratio (SNR) of event-related potentials (ERPs). However, across-trial variability of ERP latency and amplitude may contain physiologically relevant information that is lost by across-trial averaging. Hence, we aimed to develop a novel method that uses 1) wavelet filtering (WF) to enhance the SNR of ERPs and 2) a multiple linear regression with a dispersion term (MLRd) that takes into account shape distortions to estimate the single-trial latency and amplitude of ERP peaks. Using simulated ERP data sets containing different levels of noise, we provide evidence that, compared with other approaches, the proposed WF_MLRd method yields the most accurate estimate of single-trial ERP features. When applied to a real laser-evoked potential data set, the WF MLRd approach provides reliable estimation of single-trial latency, amplitude, and morphology of ERPs and thereby allows performing meaningful correlations at single-trial level. We obtained three main findings. First, WF significantly enhances the SNR of single-trial ERPs. Second, MLRd effectively captures and measures the variability in the morphology of single-trial ERPs, thus providing an accurate and unbiased estimate of their peak latency and amplitude. Third, intensity of pain perception significantly correlates with the single-trial estimates of N2 and P2 amplitude. These results indicate that WF_MLRd can be used to explore the dynamics between different ERP features, behavioral variables, and other neuroimaging measures of brain activity, thus providing new insights into the functional significance of the different brain processes underlying the brain responses to sensory stimuli. © 2011 the American Physiological Society.
Persistent Identifierhttp://hdl.handle.net/10722/170188
ISSN
2015 Impact Factor: 2.653
2015 SCImago Journal Rankings: 2.230
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorHu, Len_US
dc.contributor.authorLiang, Men_US
dc.contributor.authorMouraux, Aen_US
dc.contributor.authorWise, RGen_US
dc.contributor.authorHu, Yen_US
dc.contributor.authorIannetti, GDen_US
dc.date.accessioned2012-10-30T06:05:57Z-
dc.date.available2012-10-30T06:05:57Z-
dc.date.issued2011en_US
dc.identifier.citationJournal Of Neurophysiology, 2011, v. 106 n. 6, p. 3216-3229en_US
dc.identifier.issn0022-3077en_US
dc.identifier.urihttp://hdl.handle.net/10722/170188-
dc.description.abstractAcross-trial averaging is a widely used approach to enhance the signal-to-noise ratio (SNR) of event-related potentials (ERPs). However, across-trial variability of ERP latency and amplitude may contain physiologically relevant information that is lost by across-trial averaging. Hence, we aimed to develop a novel method that uses 1) wavelet filtering (WF) to enhance the SNR of ERPs and 2) a multiple linear regression with a dispersion term (MLRd) that takes into account shape distortions to estimate the single-trial latency and amplitude of ERP peaks. Using simulated ERP data sets containing different levels of noise, we provide evidence that, compared with other approaches, the proposed WF_MLRd method yields the most accurate estimate of single-trial ERP features. When applied to a real laser-evoked potential data set, the WF MLRd approach provides reliable estimation of single-trial latency, amplitude, and morphology of ERPs and thereby allows performing meaningful correlations at single-trial level. We obtained three main findings. First, WF significantly enhances the SNR of single-trial ERPs. Second, MLRd effectively captures and measures the variability in the morphology of single-trial ERPs, thus providing an accurate and unbiased estimate of their peak latency and amplitude. Third, intensity of pain perception significantly correlates with the single-trial estimates of N2 and P2 amplitude. These results indicate that WF_MLRd can be used to explore the dynamics between different ERP features, behavioral variables, and other neuroimaging measures of brain activity, thus providing new insights into the functional significance of the different brain processes underlying the brain responses to sensory stimuli. © 2011 the American Physiological Society.en_US
dc.languageengen_US
dc.relation.ispartofJournal of Neurophysiologyen_US
dc.subject.meshAdulten_US
dc.subject.meshAnalysis Of Varianceen_US
dc.subject.meshBrain - Physiologyen_US
dc.subject.meshComputer Simulationen_US
dc.subject.meshElectroencephalographyen_US
dc.subject.meshEvoked Potentials, Somatosensory - Physiologyen_US
dc.subject.meshFemaleen_US
dc.subject.meshHumansen_US
dc.subject.meshLasers - Adverse Effectsen_US
dc.subject.meshLinear Modelsen_US
dc.subject.meshMaleen_US
dc.subject.meshNeuroimagingen_US
dc.subject.meshPain - Etiology - Physiopathologyen_US
dc.subject.meshPhysical Stimulationen_US
dc.subject.meshReaction Time - Physiologyen_US
dc.subject.meshSignal Processing, Computer-Assisteden_US
dc.subject.meshSignal-To-Noise Ratioen_US
dc.subject.meshYoung Adulten_US
dc.titleTaking into account latency, amplitude, and morphology: Improved estimation of single-trial ERPs by wavelet filtering and multiple linear regressionen_US
dc.typeArticleen_US
dc.identifier.emailHu, Y:yhud@hku.hken_US
dc.identifier.authorityHu, Y=rp00432en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1152/jn.00220.2011en_US
dc.identifier.pmid21880936-
dc.identifier.scopuseid_2-s2.0-83055179230en_US
dc.identifier.hkuros202315-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-83055179230&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume106en_US
dc.identifier.issue6en_US
dc.identifier.spage3216en_US
dc.identifier.epage3229en_US
dc.identifier.isiWOS:000298345000040-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridHu, L=34770075600en_US
dc.identifier.scopusauthoridLiang, M=10239108300en_US
dc.identifier.scopusauthoridMouraux, A=6602503125en_US
dc.identifier.scopusauthoridWise, RG=35394428700en_US
dc.identifier.scopusauthoridHu, Y=7407116091en_US
dc.identifier.scopusauthoridIannetti, GD=7005461102en_US
dc.identifier.citeulike10107520-

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