Nuevo Enfoque para la Clasificación de Señales EEG usando la Varianza de la Diferencia entre las Clases de un Clasificador Bayesiano

Thomaz R. Botelho, Douglas Soprani, Camila Rodrigues, André Ferreira, Anselmo Frizera


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

Texto completo:



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