Non-supervised Feature Selection: Evaluation in a BCI for Single-Trial Recognition of Gait Preparation/Stop

Denis Delisle-Rodriguez, Ana Cecilia Villa-Parra, Alberto López-Delis, Anselmo Frizera-Neto, Eduardo Rocon, Teodiano Freire-Bastos

Resultado de la investigación: Capítulo del libro/informe/acta de congresoCapítulorevisión exhaustiva

Resumen

Is presented a non-supervised method for feature selection based on similarity index, which is applied in a brain-computer interface (BCI) to recognize gait preparation/stops. Maximal information compression index is here used to obtain redundancies, while representation entropy value is employed to find the feature vectors with high entropy. EEG signals of six subjects were acquired on the primary cortex during walking, in order to evaluate this approach in a BCI. The maximum accuracy was 55% and 85% to recognize gait preparation/stops, respectively. Thus, this method can be used in a BCI to improve the time delay during dimensionality reduction.

Idioma originalInglés
Título de la publicación alojadaBiosystems and Biorobotics
EditorialSpringer International Publishing
Páginas1469-1474
Número de páginas6
DOI
EstadoPublicada - 2017

Serie de la publicación

NombreBiosystems and Biorobotics
Volumen15
ISSN (versión impresa)2195-3562
ISSN (versión digital)2195-3570

Nota bibliográfica

Publisher Copyright:
© 2017, Springer International Publishing AG.

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