2.50
Hdl Handle:
http://hdl.handle.net/10547/333126
Title:
Supervised ANN vs. unsupervised SOM to classify EEG data for BCI: why can GMDH do better?
Authors:
al-Ketbi, Omar; Conrad, Marc
Abstract:
Construction of a system for measuring the brain activity (electroencephalogram (EEG)) and recognising thinking patterns comprises significant challenges, in addition to the noise and distortion present in any measuring technique. One of the most major applications of measuring and understanding EGG is the brain-computer interface (BCI) technology. In this paper, ANNs (feedforward back -prop and Self Organising Maps) for EEG data classification will be implemented and compared to abductive-based networks, namely GMDH (Group Methods of Data Handling) to show how GMDH can optimally (i.e. noise and accuracy) classify a given set of BCI’s EEG signals. It is shown that GMDH provides such improvements. In this endeavour, EGG classification based on GMDH will be researched for comprehensible classification without scarifying accuracy. GMDH is suggested to be used to optimally classify a given set of BCI’s EEG signals. The other areas related to BCI will also be addressed yet within the context of this purpose.
Affiliation:
University of Bedfordshire
Citation:
al-Ketbi, O., Conrad, M. (2013) 'Supervised ANN vs. Unsupervised SOM to Classify EEG Data for BCI: Why can GMDH do better?', International Journal of Computer Applications 74 (4):37
Publisher:
IJCA Journal
Journal:
International Journal of Computer Applications
Issue Date:
2013
URI:
http://hdl.handle.net/10547/333126
DOI:
10.5120/12876-9901
Additional Links:
http://research.ijcaonline.org/volume74/number4/pxc3889901.pdf
Type:
Article
Language:
en
ISSN:
0975-8887
Appears in Collections:
Centre for Research in Distributed Technologies (CREDIT)

Full metadata record

DC FieldValue Language
dc.contributor.authoral-Ketbi, Omaren
dc.contributor.authorConrad, Marcen
dc.date.accessioned2014-10-24T11:07:36Z-
dc.date.available2014-10-24T11:07:36Z-
dc.date.issued2013-
dc.identifier.citational-Ketbi, O., Conrad, M. (2013) 'Supervised ANN vs. Unsupervised SOM to Classify EEG Data for BCI: Why can GMDH do better?', International Journal of Computer Applications 74 (4):37en
dc.identifier.issn0975-8887-
dc.identifier.doi10.5120/12876-9901-
dc.identifier.urihttp://hdl.handle.net/10547/333126-
dc.description.abstractConstruction of a system for measuring the brain activity (electroencephalogram (EEG)) and recognising thinking patterns comprises significant challenges, in addition to the noise and distortion present in any measuring technique. One of the most major applications of measuring and understanding EGG is the brain-computer interface (BCI) technology. In this paper, ANNs (feedforward back -prop and Self Organising Maps) for EEG data classification will be implemented and compared to abductive-based networks, namely GMDH (Group Methods of Data Handling) to show how GMDH can optimally (i.e. noise and accuracy) classify a given set of BCI’s EEG signals. It is shown that GMDH provides such improvements. In this endeavour, EGG classification based on GMDH will be researched for comprehensible classification without scarifying accuracy. GMDH is suggested to be used to optimally classify a given set of BCI’s EEG signals. The other areas related to BCI will also be addressed yet within the context of this purpose.en
dc.language.isoenen
dc.publisherIJCA Journalen
dc.relation.urlhttp://research.ijcaonline.org/volume74/number4/pxc3889901.pdfen
dc.rightsArchived with thanks to International Journal of Computer Applicationsen
dc.subjectelectroencephalogramen
dc.subjectEEGen
dc.subjectbrain-computer interfaceen
dc.subjectBCIen
dc.subjectdata classificationen
dc.subjectGMDHen
dc.subjectANNen
dc.subjectsupervised ANNen
dc.subjectUnsupervised SOMen
dc.subjectpattern recognitionen
dc.subjectdata miningen
dc.titleSupervised ANN vs. unsupervised SOM to classify EEG data for BCI: why can GMDH do better?en
dc.typeArticleen
dc.contributor.departmentUniversity of Bedfordshireen
dc.identifier.journalInternational Journal of Computer Applicationsen
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