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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ruiting Yang Gray, D.A. Ng, B.W. Mingyi He |
| Copyright Year | 2009 |
| Description | Author affiliation: Northwestern Polytechnical University, Xi'an, China (Mingyi He) || School of Electrical and Electronics Engineering, University of Adelaide, Australia (Ruiting Yang; Gray, D.A.; Ng, B.W.) |
| Abstract | Brain computer interface (BCI) systems utilise Electroencephalography (EEG) to translate specific human thinking activities into control commands. An essential part of any BCI is a pattern recognition system. In this paper, a number of different features and classifiers are compared in terms of classification accuracy and computation time. Two typical features are studied: autoregressive (AR) and spectrum components along with three different classifiers; the K-nearest neighbor, linear discriminant analysis (LDA) and Bayesian statistical classifiers. The results showed that all classifiers achieved very high accuracies and short computation times. |
| Starting Page | 580 |
| Ending Page | 585 |
| File Size | 462216 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424427994 |
| DOI | 10.1109/ICIEA.2009.5138215 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-05-25 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Signal analysis Signal processing Brain computer interfaces Electroencephalography Rhythm Signal processing algorithms Feature extraction Frequency Pattern recognition Humans classifier Electroencephalography (EEG) brain computer interface feature |
| Content Type | Text |
| Resource Type | Article |
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