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dc.contributor.authorHema, Chengalvarayan Radhakrishnamurthy-
dc.contributor.authorMurugesa Pandiyan, Paulraj, Assoc. Prof. Dr.-
dc.date.accessioned2011-10-05T15:52:08Z-
dc.date.available2011-10-05T15:52:08Z-
dc.date.issued2011-06-20-
dc.identifier.citationIFMBE Proceedings, vol. 35 (9), 2011, pages 287-291en_US
dc.identifier.isbn978-364221728-9-
dc.identifier.issn1680-0737-
dc.identifier.urihttp://www.springerlink.com/content/h443052035k7511n/-
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/14037-
dc.descriptionLink to publisher's homepage at http://www.springerlink.com/en_US
dc.description.abstractControlling a power wheelchair using a brain machine interface (BMI) requires sufficient subject training. A neural network based BMI design using motor imagery of four states is used to control the navigation of a power wheelchair. The online experiment results are presented for two indoor navigation protocols. Twotrained subjects participated in the study. Performance of the real-time experiments is assessed based on the targets reached, time taken to reach the targets and on completion of a given navigation protocol. A performance rate of 85.7% is achievable by both subjects for the real-time experiments.en_US
dc.language.isoenen_US
dc.publisherSpringer-Verlagen_US
dc.relation.ispartofseriesProceedings of the 5th Kuala Lumpur International Conference on Biomedical Engineering (BIOMED 2011)en_US
dc.subjectBrain machine interface (BMI)en_US
dc.subjectEEG signal processingen_US
dc.subjectNeural networksen_US
dc.titleControl brain machine interface for a power wheelchairen_US
dc.typeWorking Paperen_US
dc.contributor.urlpaul@unimap.edu.myen_US
Appears in Collections:Conference Papers
Paulraj Murugesa Pandiyan, Assoc. Prof. Dr.

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