Comparative study of feature extraction methods and classification of event-related potentials P300
Keywords:
Brain-Computer Interface, BCI, P300, Análisis de Componentes Principales PCA, Wavelet transform, LSVM, QSVM, KNN, EEG.Abstract
The objective of the present work is to evaluate the potential of two types of classifiers, Vector Support Machines SVM and K-Nearest Neighbors KNN, to detect event-related potentials (P300). These two classifiers are trained and assessed with morphological characteristics and with the approximation coefficients of the Wavelet Discrete transformation. Before feature extraction, electroencephalographic (EEG) signals were processed to remove artifacts, filtered, normalized and segmented. This whole process was performed for samples with the synchronized average of 15 P300 signals and samples of only one P300 signal. In the final part of the document, we present a comparative analysis of the results and propose alternatives that could contribute to an improvement of the classification accuracy in future works.Metrics
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