Translation of electroencephalographic (EEG) recordings into control signals for brain-computer interface (BCI) systems needs to be based on a robust classification of the various types of information. EEG-based BCI features are often noisy and likely to contain outliers. This contribution describes
Support vector machines of interval-based features for time series classification
✍ Scribed by Juan José Rodríguez; Carlos J. Alonso; José A. Maestro
- Publisher
- Elsevier Science
- Year
- 2005
- Tongue
- English
- Weight
- 174 KB
- Volume
- 18
- Category
- Article
- ISSN
- 0950-7051
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