Extracción de características en señales MER para el reconocimiento de zonas cerebrales

Abstract

We present a methodology for dynamic feature extraction by means of adaptive filter banks in case of automatic identification of brain zone using micro electrode recording. Proposed biorthogonal filter banks changes according energy. Besides, adaptive lifting schemes, which allow filter order change, are used for filter bank implementation. Lifting schemes are introduced because lower computational complexity and less processing time. As features, both maximum value and variance of different wavelet decomposition levels are selected for brain zone classification. As a result, classification performance level of 98.5% value, estimated by means of bayesian classifier with Mahalanobis distance, is reached which is better than in 5% in comparison to those obtained figures for filter banks but having fixed parameters. © Springer-Verlag Berlin Heidelberg 2007.