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Título: | Motor intention recognition in EEG : in pursuit of a relevant feature set |
Autor: | Iturralde, Pablo Patrone, Martín Lecumberry, Federico Fernández, Alicia |
Tipo: | Ponencia |
Fecha de publicación: | 2012 |
Resumen: | Brain-computer interfaces (BCIs) based on electroencephalograms (EEG) are a noninvasive and cheap alternative to get a communication channel between brain and computers. Some of the main issues with EEG signals are its high dimensionality, high inter-user variance, and non-stationarity. In this work we present different approaches to deal with the high dimensionality of the data, finding relevant descriptors in EEG signals for motor intention recognition: first, a classical dimensionality reduction method using Diffusion Distance, second a technique based on spectral analysis of EEG channels associated with the frontal and prefrontal cortex, and third a projection over average signals. Performance analysis for different sets of features is done, showing that some of them are more robust to user variability. |
Descripción: | Trabajo presentado en Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications (CIARP 2012) |
Citación: | Iturralde, P, Patrone, M, Lecumberry, F, Fernández, A. "Recognition in EEG: in pursuit of a relevant feature set". Publicado en: Alvarez, L., Mejail, M., Gomez, L., Jacobo, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2012. Lecture Notes in Computer Science, vol 7441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33275-3_68 |
Departamento académico: | Procesamiento de Señales |
Grupo de investigación: | Tratamiento de Imágenes |
Licencia: | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
Aparece en las colecciones: | Publicaciones académicas y científicas - Instituto de Ingeniería Eléctrica |
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IPLF2012.pdf | 430,78 kB | Adobe PDF | Visualizar/Abrir |
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