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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Cavrini, F. Saggio, G. Bianchi, L. Quitadamo, L.R. Abbafati, M. |
| Copyright Year | 2011 |
| Description | Author affiliation: Department of Computer Science, Systems and Production, University of Rome “Tor Vergata”, Italy (Cavrini, F.) || Department of Electronic Engineering, University of Rome “Tor Vergata”, Italy (Abbafati, M.) || Department of Neuroscience, University of Rome “Tor Vergata”, Italy (Saggio, G.; Bianchi, L.; Quitadamo, L.R.) |
| Abstract | One of the key issues in the development of brain-computer interfaces (BCIs) is the improvement of their current information transfer rate. In order to achieve that objective at least two aspects of BCI design should be considered: classification accuracy and protocol specification. In this paper we show how combination of classifiers using fuzzy measures and the Choquet integral can be applied to the context of visual P300 BCI in order to lower the number of misclassifications. Results of an offline analysis are provided and possible benefits in terms of the information transfer rate are briefly discussed. |
| Starting Page | 1 |
| Ending Page | 5 |
| File Size | 885274 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424493364 |
| e-ISBN | 9781424493388 |
| DOI | 10.1109/MeMeA.2011.5966688 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-05-30 |
| Publisher Place | Italy |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Electroencephalography Finite element methods Support vector machine classification Indexes Classification algorithms Pattern recognition P300 Brain computer interfaces (BCIs) Combination of classifiers Electroencephalography (EEG) Fuzzy integral Fuzzy measure |
| Content Type | Text |
| Resource Type | Article |
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