Selección de Electrodos Basada en k-means para la Clasificación de Actividad Motora en EEG.

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Bibliographic Details
Title:	Selección de Electrodos Basada en k-means para la Clasificación de Actividad Motora en EEG.
Authors:	Lemuz-López, R.¹ rafalemuz@gmail.com, Gómez-López, W.¹, Ayaquica-Martínez, I.¹, Guillén-Galván, C.¹
Source:	Revista Mexicana de Ingeniería Biomédica. ago2014, Vol. 35 Issue 2, p1-9. 9p. 1 Color Photograph, 1 Diagram, 2 Charts, 3 Graphs.
Abstract (English):	We present an algorithm for electrodes selection associated with motor imagery activity. The algorithm uses a clustering technique called kmeans to form groups of sensors and selects the group corresponding to the highest correlation activity. Then, we evaluate the selected electrodes computing the classification index using the projective decomposition called common spatial patterns and a linear discriminant method in a left hand vs right foot motor imagery classification task. This approach significantly reduces the number of electrodes from 118 to 35 while improving the classification accuracy index. [ABSTRACT FROM AUTHOR]
Abstract (Spanish):	Se presenta un algoritmo para la selección del grupo de electrodos relacionados con la imaginación de movimiento. El algoritmo utiliza la técnica de agrupamiento llamada k-means para formar grupos de sensores y selecciona el grupo que corresponde a la actividad correlacionada más alta. Para evaluar la selección de electrodos, se calcula el indice de clasificación aplicando la descomposición proyectiva llamada patrones espaciales comunes y un discriminante lineal en una prueba de una sola época para identificar la imaginación del movimiento de mano izquierda vs pie derecho. Esta propuesta reduce significativamente el número de electrodos de 118 a 35, además de mejorar el índice de clasificación. [ABSTRACT FROM AUTHOR]
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Database:	MedicLatina

Description
Abstract:	We present an algorithm for electrodes selection associated with motor imagery activity. The algorithm uses a clustering technique called kmeans to form groups of sensors and selects the group corresponding to the highest correlation activity. Then, we evaluate the selected electrodes computing the classification index using the projective decomposition called common spatial patterns and a linear discriminant method in a left hand vs right foot motor imagery classification task. This approach significantly reduces the number of electrodes from 118 to 35 while improving the classification accuracy index. [ABSTRACT FROM AUTHOR]
ISSN:	01889532