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dc.contributor.authorPaulraj, Murugesapandian
dc.contributor.authorHema, Chengalvarayan Radhakrishnamurthy
dc.contributor.authorRamachandran, Nagarajan
dc.contributor.authorSazali, Yaacob
dc.contributor.authorAbdul Hamid, Adom
dc.date.accessioned2009-08-10T03:47:05Z
dc.date.available2009-08-10T03:47:05Z
dc.date.issued2007-12
dc.identifier.citationp.1-5en_US
dc.identifier.isbn978-1-4244-1469-7
dc.identifier.urihttp://ieeexplore.ieee.org/xpls/abs_all.jsp?=&arnumber=4451377
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/6763
dc.descriptionLink to publisher's homepage at http://ieeexplore.ieee.orgen_US
dc.description.abstractBrain Machine Interfaces use the cognitive abilities of patients with neuromuscular disorders to restore communication and motor functions. At present, only EEG and related methods, which have relatively short time constants, can function in most environments, they also require relatively simple and inexpensive equipment. In this paper we propose a mental task classification algorithm using a Particle Swarm Optimization (PSO) for a Radial basis Neural Network. Features are extracted from EEG signals that are recorded during five mental tasks, namely baseline-resting, mathematical multiplication, geometric figure rotation, letter composing and visual counting. PCA features extracted from the task signals are used the neural net to classify different combinations of two mental tasks. Results obtained show average classification rates ranging from % to %.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineering (IEEE)en_US
dc.relation.ispartofseries5th Student Conference on Research and Development (SCORED 2007)en_US
dc.subjectEEG signal processingen_US
dc.subjectParticle swarm optimizationen_US
dc.subjectPCAen_US
dc.subjectRadial basis function neural networksen_US
dc.subjectMedical computingen_US
dc.subjectNeurophysiologyen_US
dc.subjectElectroencephalographyen_US
dc.titleEEG classification using radial basis PSO neural network for brain machine interfacesen_US
dc.typeArticleen_US


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