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Inicio Revista Iberoamericana de Automática e Informática Industrial RIAI Nuevo Enfoque para la Clasificación de Señales EEG usando la Varianza de la Di...
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Vol. 14. Núm. 4.
Páginas 362-371 (Octubre - Diciembre 2017)
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Vol. 14. Núm. 4.
Páginas 362-371 (Octubre - Diciembre 2017)
Open Access
Nuevo Enfoque para la Clasificación de Señales EEG usando la Varianza de la Diferencia entre las Clases de un Clasificador Bayesiano
New Approach to the EEG Signals Classification using the Variance of the Difference between the Classes of a Bayesian Classifier
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Thomaz R. Botelhoa,b,
Autor para correspondencia
thomazrb@ifes.edu.br

Autor para correspondencia.
, Douglas Sopranib, Camila Rodriguesa, André Ferreiraa, Anselmo Frizeraa
a Programa de Posgrado en Ingeniería Eléctrica, Universidad Federal de Espírito Santo (UFES), Av. Fernando Ferrari, 514, Vitória-ES, Brasil
b Departamento de Electrotecnología, Instituto Federal de Educación, Ciencia y Tecnología de Espírito Santo (IFES), BR 101 Av. Norte, 58, São Mateus -ES, Brasil
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Los avances en robótica de rehabilitación están beneficiando en gran medida a los pacientes con discapacidad física. Los dispositivos de asistencia y rehabilitación pueden basar su funcionamiento en información fisiológica de los músculos y del cerebro a través de electromiografía (EMG) y electroencefalografía (EEG), para detectar la intención de movimiento de los usuarios. En este trabajo se presenta una propuesta de interfaz multimodal para la adquisición, sincronización y procesamiento de señales EEG y de sensores inerciales, para ser aplicada en tareas de rehabilitación con exoesqueletos robóticos. Se realizaron experimentos con individuos sanos con el objetivo de analizar la intención de movimiento, la activación muscular e inicio de movimiento durante los movimientos de extensión de la rodilla. Esta propuesta es un nuevo enfoque para la clasificación de señales EEG usando un clasificador bayesiano tomando en cuenta la varianza de la diferencia entre las clases usadas. El aporte de este trabajo se sustenta con los resultados que muestran un incremento del 30% en la precisión de clasificación con señales EEG en comparación con los enfoques tradicionales de clasificación, en un análisis off-line para el reconocimiento de la intención de movimiento de los miembros inferiores.

Palabras clave:
Interfaz hombre-máquina
Análisis de señales
Sistemas biomédicos
Unidades de medición inercial
Cerebro humano
Movimiento
Abstract

Patients with physical disabilities can benefit from robotic rehabilitation. This improves the efficiency of recovery and, therefore, the rehabilitation of the patient. Assistive and rehabilitation devices can make use of physiological data, such as electromyography (EMG) and electroencephalography (EEG), in order to detect movement intentions. This work presents a multimodal interface for signal acquisition, synchronization and processing of EEG and inertial sensors signals, to be applied in rehabilitation robotic exoskeletons. Experiments were performed with healthy individuals executing knee extension. The goal is to analyze movement intention, muscle activation and movement onset. It was proposed a new approach to the EEG signals classification using a Bayesian classifier taking into account the variance of the difference between the classes used. This contribution presents an average improvement of about 30% in the EEG classification accuracy in comparison to the traditional classifier approach. In this work an offline analysis was conducted.

Keywords:
Human-machine interface
Signal analysis
Biomedical systems
Inertial measurement units
Human brain
Movement
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