Classification of EEG recordings by using fast independent component analysis and artificial neural network

dc.contributor.author	Kocyigit, Y.
dc.contributor.author	Alkan, A.
dc.contributor.author	Erol, H.
dc.date.accessioned	2021-01-25T19:36:49Z
dc.date.available	2021-01-25T19:36:49Z
dc.date.issued	2008
dc.identifier	10.1007/s10916-007-9102-z
dc.identifier.issn	0148-5598
dc.identifier.uri	https://dspace.yasar.edu.tr/xmlui/handle/20.500.12742/8378
dc.description.abstract	Since there is no definite decisive factor evaluated by the experts, visual analysis of EEG signals in time domain may be inadequate. Routine clinical diagnosis requests to analysis of EEG signals. Therefore, a number of automation and computer techniques
dc.language.iso	English
dc.publisher	SPRINGER
dc.title	Classification of EEG recordings by using fast independent component analysis and artificial neural network
dc.type	Article
dc.relation.firstpage	17
dc.relation.lastpage	20
dc.relation.volume	32
dc.relation.issue	1
dc.description.woscategory	Health Care Sciences & Services; Medical Informatics
dc.description.wosresearcharea	Health Care Sciences & Services; Medical Informatics
dc.identifier.wosid	WOS:000252168100003

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