EEG signal classification for real-time brain-computer interface applications: A review

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dc.contributor.authorKhorshidtalab, A.
dc.contributor.authorSalami, Momoh-Jimoh E.
dc.date.accessioned2019-10-24T10:38:40Z
dc.date.available2019-10-24T10:38:40Z
dc.date.issued2011-05-17
dc.description.abstractBrain-computer interface (BCI) is linking the brain activity to computer, which allows a person to control devices directly with his brain waves and without any use of his muscles. Recent advances in real-time signal processing have made BCI a feasible alternative for controlling robot and for communication as well. Controlling devices using BCI is a crucial aid for people suffering from severe disabilities and more than that, BCIs can replace human to control robots working in dangerous or uncongenial situations. Effective BCIs demand for accurate and real-time EEG signals processing. This paper is to review the current state of research and to compare the performance of different algorithms for realtime classification of BCI-based electroencephalogram signals.en_US
dc.identifier.citationKhorshidtalab, A., & Salami, M. J. E. (2011, May). EEG signal classification for real-time brain-computer interface applications: A review. In 2011 4th International Conference on Mechatronics (ICOM) (pp. 1-7). IEEE.en_US
dc.identifier.uri10.1109/ICOM.2011.5937154
dc.identifier.urihttp://repository.elizadeuniversity.edu.ng/handle/20.500.12398/597
dc.language.isoenen_US
dc.publisher2011/5/17en_US
dc.subjectElectroencephalographyen_US
dc.subjectClassification algorithmsen_US
dc.subjectFeature extractionen_US
dc.subjectBrain computer interfacesen_US
dc.subjectHidden Markov modelsen_US
dc.subjectReal time systemsen_US
dc.subjectSupport vector machinesen_US
dc.titleEEG signal classification for real-time brain-computer interface applications: A reviewen_US
dc.typeArticleen_US
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