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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Besio, W.G. Hongbao Cao Peng Zhou |
| Copyright Year | 2001 |
| Abstract | For persons with severe disabilities, a brain-computer interface (BCI) may be a viable means of communication. Lapalacian electroencephalogram (EEG) has been shown to improve classification in EEG recognition. In this work, the effectiveness of signals from tripolar concentric electrodes and disc electrodes were compared for use as a BCI. Two sets of left/right hand motor imagery EEG signals were acquired. An autoregressive (AR) model was developed for feature extraction with a Mahalanobis distance based linear classifier for classification. An exhaust selection algorithm was employed to analyze three factors before feature extraction. The factors analyzed were 1) length of data in each trial to be used, 2) start position of data, and 3) the order of the AR model. The results showed that tripolar concentric electrodes generated significantly higher classification accuracy than disc electrodes. |
| Sponsorship | IEEE Engineering in Medicine and Biology Society |
| Page Count | 4 |
| File Size | 289468 |
| Starting Page | 191 |
| Ending Page | 194 |
| File Format | |
| ISSN | 15344320 |
| Volume Number | 16 |
| Issue Number | 2 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-04-01 |
| Publisher Place | U.S.A. |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Electroencephalography Laplace equations Biomedical engineering Brain modeling Feature extraction Biomedical electrodes Frequency Spline Algorithm design and analysis Parameter estimation tripolar electrode Brain–computer interface (BCI) electroencephalogram (EEG) classification Laplacian estimation parameter selection BCI EEG classification |
| Content Type | Text |
| Resource Type | Article |
| Subject | Neuroscience Rehabilitation Internal Medicine Biomedical Engineering Computer Science Applications |
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