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dc.contributor.authorVijean, vikneswaran
dc.contributor.authorHariharan, Muthusamy, Dr.
dc.contributor.authorSazali, Yaacob, Prof. Dr.
dc.contributor.authorMohd Nazri, Sulaiman
dc.contributor.authorAbdul Hamid, Adom, Prof. Dr.
dc.date.accessioned2014-06-18T02:27:25Z
dc.date.available2014-06-18T02:27:25Z
dc.date.issued2013-07
dc.identifier.citationComputers and Electrical Engineering, vol. 39(5), 2013, pages 1549-1560en_US
dc.identifier.issn0045-7906
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S0045790613000025
dc.identifier.urihttp://dspace.unimap.edu.my:80/dspace/handle/123456789/35665
dc.descriptionLink to publisher's homepage at http://www.elsevier.com/en_US
dc.description.abstractVisually evoked potentials (VEPs) originate from the occipital cortex and have long been used as a reliable indicator for vision impairments by ophthalmologists. Any abnormalities in the visual pathways of a person can be diagnosed by analyzing these responses. The amplitudes and latency of VEP responses have been traditionally used for the diagnosis of vision impairments. This paper proposes new ways in which to analyze VEP responses by investigating the time and frequency domain characteristics of the signals. The single trial VEP's are decomposed into six different frequency bands; delta, theta, alpha, beta, gamma1 and gamma2, using digital elliptic filters. Statistical features are extracted from the decomposed VEP's and are analyzed using student two tailed t-test and box plot analysis. Levenberg-Marquardt backpropagation neural network (LMBP) and Extreme Learning Machine (ELM) algorithms are employed for the discrimination of vision impairment. The proposed method gives promising classification accuracy ranging from 90.90% to 96.89%.en_US
dc.language.isoenen_US
dc.publisherElsevier Ltd.en_US
dc.subjectClassification accuracyen_US
dc.subjectDifferent frequencyen_US
dc.subjectExtreme learning machineen_US
dc.subjectLevenberg-Marquardten_US
dc.titleObjective investigation of vision impairments using single trial pattern reversal visually evoked potentialsen_US
dc.typeArticleen_US
dc.contributor.urlv.vikneswaran@ieee.orgen_US
dc.contributor.urlhari@unimap.edu.myen_US
dc.contributor.urls.yaacob@unimap.edu.myen_US
dc.contributor.urlnazri_sulaiman@hotmail.comen_US
dc.contributor.urlabdhamid@unimap.edu.myen_US


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