Caracterización de Señales EEG mediante Wavelet Packet y Entropía Difusa para Tareas de Imaginación Motora

Characterization of EEG Signals Using Wavelet Packet and Fuzzy Entropy in Motor Imagination Tasks

  • Boris Alexander Medina Universidad de Sucre
  • Ramón Alvarez López Universidad de Sucre
Palabras clave: BCI, EEG, Wavelet Packet. (en_US)
Palabras clave: BCI, EEG, WaveletPacket (es_ES)

Resumen (es_ES)

Contexto: el análisis de ritmos clínicos en los métodos de procesamiento de señales avanzadas es de mucho interés en áreas médicas, tales como el diagnóstico de los trastornos cerebrales, la epilepsia, el análisis del sueño o la anestesia y, más recientemente, en las interfaces cerebro computador (BCI).


Método: en este trabajo se aplica la Transformada Wavelet Packet a fin de extraer los ritmos cerebrales de señales electroencefalográficas (EEG) relacionadas a tareas de imaginación motora, contenidas en la base de datos de la competencia BCI 2008. Usando funciones estadísticas ampliamente aplicadas en la literatura, se obtiene la matriz de datos que caracteriza los ritmos cerebrales, que son discriminadas mediante diferentes clasificadores y evaluados usando criterio de validación cruzada de diez pliegues.


Resultados: la exactitud de clasificación se acerca al 81.11% en promedio, con un grado de acuerdo de 61%, lo que indica una concordancia adecuada, como ha sido previamente reportada en la literatura. Un análisis de relevancia mostr-Spanish-ó la concentración de características aportadas en los nodos producto de la descomposición Wavelet, así como las características que mayor contenido de información contribuyen a mejorar la región de decisión de separabilidad para la tarea de clasificación.

Conclusiones: el método propuesto puede ser utilizado como referencia para apoyar futuros estudios en la tarea de caracterización de señales EEG orientadas a la imaginación de movimiento de la mano derecha e iz-quierda, teniendo en cuenta que nuestros resultados demostraron ser favorables en comparación con los pro-puestos en la literatura.

Idioma: Español

Resumen (en_US)

Context:  Clinical rhythm analysis on advanced signal processing methods is very important in medical areas such as brain disorder diagnostic, epilepsy, sleep analysis, anesthesia analysis, and more recently in brain-computer interfaces (BCI).

Method: Wavelet transform package is used on this work to extract brain rhythms of electroencephalographic signals (EEG) related to motor imagination tasks. We used the Competition BCI 2008 database for this characterization. Using statistical functions we obtained features that characterizes brain rhythms, which are discriminated using different classifiers; they were evaluated using a 10-fold cross validation criteria.

Results: The classification accuracy achieved 81.11% on average, with a degree of agreement of 61%, indicating a "suitable" concordance, as it has been reported in the literature. An analysis of relevance showed the concentration of characteristics provided in the nodes as a result of Wavelet decomposition, as well as the characteristics that more information content contribute to improve the separability decision region for the classification task.

Conclusions: The proposed method can be used as a reference to support future studies focusing on characterizing EEG signals oriented to the imagination of left and right hand movement, considering that our results proved to compared favourably to those reported in the literature.

Language: Spanish.

 

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Biografía del autor/a

Boris Alexander Medina, Universidad de Sucre
Ingeniero Electónico, Magister en Automatización con linea de profundización en Reconocimiento de patrones. Docente adscrito al departamento de Ingeniería de la Universidad de Sucre.
Ramón Alvarez López, Universidad de Sucre
Ingeniero Electrónico, Magister en Controles Industriales, Doctor en Ingeniería de la Universidad Nacional de Colombia. Actualmente adscrito a la facultad de Ingeniería de la Universidad de Sucre.

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Cómo citar
Medina, B. A., & Alvarez López, R. (2017). Caracterización de Señales EEG mediante Wavelet Packet y Entropía Difusa para Tareas de Imaginación Motora. Ingeniería, 22(2), 226-238. https://doi.org/10.14483/udistrital.jour.reving.2017.2.a04
Publicado: 2017-05-05
Sección
Inteligencia Computacional