Unsupervised Common Spatial Patterns
The common spatial pattern (CSP) method is a dimensionality reduction technique widely used in brain-computer interface (BCI) systems. In the two-class CSP problem, training data are linearly projected onto direc tions maximizing or minimizing the variance ratio between the two classes. The present...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/131369 |
| Acceso en línea: | https://hdl.handle.net/11441/131369 https://doi.org/10.1109/TNSRE.2019.2936411 |
| Access Level: | acceso abierto |
| Palabra clave: | Common spatial patterns Brain computer interfaces Kurtosis |
| Sumario: | The common spatial pattern (CSP) method is a dimensionality reduction technique widely used in brain-computer interface (BCI) systems. In the two-class CSP problem, training data are linearly projected onto direc tions maximizing or minimizing the variance ratio between the two classes. The present contribution proves that kurto sis maximization performs CSP in an unsupervised manner, i.e., with no need for labeled data, when the classes follow Gaussian or elliptically symmetric distributions. Numerical analyses on synthetic and real data validate these findings in various experimental conditions, and demonstrate the interest of the proposed unsupervised approach. |
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