Fuzzy Entropy Relevance Analysis in DWT and EMD for BCI Motor Imagery Applications

Análisis de Relevancia con Entropía Difusa en Aplicaciones BCI con Imaginación Motora mediante Descomposiciones DWT y EMD

  • Boris Medina Salgado Universidad Distrital Francisco José de Caldas
  • Leonardo Duque Muñoz Instituto Tecnológico Metropolitano (ITM)
Palabras clave: fuzzy entropy, wavelet, EMD, BCI (en_US)

Resumen (en_US)

Rhythm analysis in advanced signal processing methods has long of interest in application areas such as diagnosis of brain disorders, epilepsy, sleep or anesthesia analysis, and more recently in brain computer interfaces. In this paper the Discrete Wavelet Transform (DWT) and Empirical Mode Decomposition (EMD) techniques are applied to extract the brain rhythms from electroencephalographic (EEG) signals in motor imagination tasks, of left-and right hand, using public dataset BCI Competition 2003. Then the brain rhythms are characterized by statistical features. Additionally, fuzzy entropy algorithm was used to perform the relevance analysis to determine the most important features in the training set. Classification stage was performed using K-NN classifiers and SVM, obtaining classification accuracy up to 100% with EMD. Classification results allow us to infer that the techniques used are appropriate to generate solutions in BCI applications for recognizing motor imagination in people with motor disabilities.

Resumen (es_ES)

El análisis de ritmos en métodos avanzados de procesamiento de señal es de interés en áreas de aplicación, tales como diagnóstico de trastornos cerebrales, epilepsia, análisis del sueño o anestesia, y más recientemente en las interfaces cerebro-computador. En este trabajo se aplican la transformada wavelet discreta (DWT) y descomposición por modos empíricos (EMD) para extraer los ritmos cerebrales de señales electroencefalográficas  (EEG) en tareas de imaginación de motora, de mano izquierda y derecha, utilizando la bases de datos publica BCI Competition 2003. Los ritmos cerebrales se caracterizan mediante funciones estadísticas; además, se utilizó el algoritmo de entropía difusa para realizar el análisis de relevancia y determinar las características más importantes en el conjunto de entrenamiento. La etapa de clasificación se realizó utilizando clasificadores K-NN y SVM, de la que se obtuvieron porcentajes de precisión de hasta el 100 % de  clasificación. Los resultados de la clasificación permiten inferir que las técnicas utilizadas son adecuadas para generar soluciones en aplicaciones BCI para el reconocimiento de la imaginación motora en las personas con discapacidad motora.

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Cómo citar
Medina Salgado, B., & Duque Muñoz, L. (2015). Análisis de Relevancia con Entropía Difusa en Aplicaciones BCI con Imaginación Motora mediante Descomposiciones DWT y EMD. Ingeniería, 20(1), 9 -19. https://doi.org/10.14483/udistrital.jour.reving.2015.1.a01
Publicado: 2015-02-18
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