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To extract the independent components of the evoked potentials in the EEG using ICA

Zervakis Michail, Clercq Wim de, Belal Suliman, Jervis Barrie, Camilleri Kenneth P., Herrero German, Bigan Cristin, Lowe David, Cassar Tracey A., Fabri Simon G., Michalopoulos Konstantinos, Bermudez T.

Απλή Εγγραφή

URIhttp://purl.tuc.gr/dl/dias/C4C8068C-93C5-4177-B477-D6204E9756F6-
Αναγνωριστικόhttp://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.124.5482&rep=rep1&type=pdf#page=25-
Γλώσσαen-
Μέγεθος1 pageen
ΤίτλοςTo extract the independent components of the evoked potentials in the EEG using ICAen
ΔημιουργόςZervakis Michailen
ΔημιουργόςΖερβακης Μιχαηλel
ΔημιουργόςClercq Wim deen
ΔημιουργόςBelal Sulimanen
ΔημιουργόςJervis Barrieen
ΔημιουργόςCamilleri Kenneth P.en
ΔημιουργόςHerrero Germanen
ΔημιουργόςBigan Cristinen
ΔημιουργόςLowe Daviden
ΔημιουργόςCassar Tracey A.en
ΔημιουργόςFabri Simon G.en
ΔημιουργόςMichalopoulos Konstantinosen
ΔημιουργόςΜιχαλοπουλος Κωνσταντινοςel
ΔημιουργόςBermudez T.en
ΠερίληψηThe aim was to develop a reliable method of extracting the independent components of single trial evoked potential (EP) signals to derive features for the subject’s bioprofile, for diagnostic, prognostic, and monitoring purposes. Single trials are of interest, because conventional averaging conceals trial-to-trial variability and hence information. Independent Components Analysis (ICA) is a technique for Blind Source Separation (BSS) to recover N temporally independent source signals s = {s1(t), ... sN(t)} from N linear mixtures (the observations), x = {x (t), ... x (t)} 1N 2. obtained by multiplying the matrix of unknown sources s by an unknown mixing matrix A, (x = A.s). ICA seeks a square unmixing matrix W such that s = W.x. Difficulties arise for short duration, relatively low amplitude EPs, which have sparse ICs. The effectiveness of different algorithms was compared. Problems associated with more sources than measurement electrodes and with the generation by the algorithms of artefactual components were investigated. Ways of extracting the true EP components were considered. Component grouping was applied to obtain reliable groups, which could be explored for any clinical interpretations. Here we describe the recommended approach as developed by our virtual research group. en
ΤύποςΣύντομη Δημοσίευση σε Συνέδριοel
ΤύποςConference Short Paperen
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2015-10-26-
Ημερομηνία Δημοσίευσης2006-
Θεματική ΚατηγορίαElectroencephalographyen
Θεματική ΚατηγορίαIndependent component analysisen
Θεματική ΚατηγορίαComputer algorithmsen
Βιβλιογραφική ΑναφοράB. Jervis, S. Belal, G. Herrero, T. Bermudez, D. Lowe, C. Bigan, K. Camilleri, T. Cassar, S. Fabri, W. De Clercq, M. Zervakis, K. Michalopoulos," presented at Biopattern Brain Workshop, Gotenborg, Sweden, 2006.en

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