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Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/C4C8068C-93C5-4177-B477-D6204E9756F6 | - |
| Identifier | http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.124.5482&rep=rep1&type=pdf#page=25 | - |
| Language | en | - |
| Extent | 1 page | en |
| Title | To extract the independent components of the evoked potentials in the EEG using ICA | en |
| Creator | Zervakis Michail | en |
| Creator | Ζερβακης Μιχαηλ | el |
| Creator | Clercq Wim de | en |
| Creator | Belal Suliman | en |
| Creator | Jervis Barrie | en |
| Creator | Camilleri Kenneth P. | en |
| Creator | Herrero German | en |
| Creator | Bigan Cristin | en |
| Creator | Lowe David | en |
| Creator | Cassar Tracey A. | en |
| Creator | Fabri Simon G. | en |
| Creator | Michalopoulos Konstantinos | en |
| Creator | Μιχαλοπουλος Κωνσταντινος | el |
| Creator | Bermudez T. | en |
| Content Summary | The 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 |
| Type of Item | Σύντομη Δημοσίευση σε Συνέδριο | el |
| Type of Item | Conference Short Paper | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2015-10-26 | - |
| Date of Publication | 2006 | - |
| Subject | Electroencephalography | en |
| Subject | Independent component analysis | en |
| Subject | Computer algorithms | en |
| Bibliographic Citation | B. 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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