Show simple item record

dc.contributor.authorHema, Chengalvarayan Radhakrishnamurthy
dc.contributor.authorPaulraj, Murugesapandian
dc.contributor.authorSazali, Yaacob
dc.contributor.authorAbdul Hamid, Adom
dc.contributor.authorNagarajan, Ramachandran
dc.date.accessioned2009-12-09T07:32:44Z
dc.date.available2009-12-09T07:32:44Z
dc.date.issued2008-08-26
dc.identifier.citationvol.3, p.1-6en_US
dc.identifier.isbn978-1-4244-2327-9
dc.identifier.urihttp://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=4631976
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/7383
dc.descriptionLink to publisher's homepage at http://ieeexplore.ieee.orgen_US
dc.description.abstractClassification of EEG mental task signals is a technique in the design of Brain machine interface [BMI]. A BMI can provide a digital channel for communication in the absence of the biological channels and are used to rehabilitate patients with neurodegenerative diseases, a condition in which all motor movements are impaired including speech leaving the patients totally locked-in. BMI are designed using the electrical activity of the brain detected by scalp EEG electrodes. In this paper five different mental tasks from two subjects were studied, combinations of two tasks are used in the classification process. A novel functional link neural network trained by a PSO algorithm is proposed for classification of the EEG signals. Principal component analysis features are used in the training and testing of the neural network. The average classification accuracies were observed to vary from 80.25% to 93% for the 10 different task combinations for each of the subjects. The proposed network has an average training time of 0.16 sec. The results obtained validate the performance of the proposed algorithm for mental task classification.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineering (IEEE)en_US
dc.relation.ispartofseriesProceedings of the International Symposium on Information Technology (ITSim 08)en_US
dc.subjectElectroencephalographyen_US
dc.subjectEEG mentalen_US
dc.subjectSignal classificationen_US
dc.subjectBrain-computer interfacesen_US
dc.subjectMedical signal processingen_US
dc.subjectPrincipal component analysisen_US
dc.subjectNeural netsen_US
dc.titleFunctional link PSO neural network based classification of EEG mental task signalsen_US
dc.typeWorking Paperen_US
dc.contributor.urlhema@unimap.edu.myen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record