masterThesis
Classificador máquina de suporte vetorial com análise de Fourier aplicada em dados de EEG e EMG
Fecha
2016-02-03Registro en:
CARVALHO, Jhonnata Bezerra de. Classificador máquina de suporte vetorial com análise de Fourier aplicada em dados de EEG e EMG. 2016. 94f. Dissertação (Mestrado em Matemática Aplicada e Estatística) - Centro de Ciências Exatas e da Terra, Universidade Federal do Rio Grande do Norte, Natal, 2016.
Autor
Carvalho, Jhonnata Bezerra de
Resumen
The classifier support vector machine is used in several problems in various areas of
knowledge. Basically the method used in this classier is to end the hyperplane that
maximizes the distance between the groups, to increase the generalization of the classifier. In this work, we treated some problems of binary classification of data obtained by electroencephalography (EEG) and electromyography (EMG) using Support Vector Machine with some complementary techniques, such as: Principal Component Analysis to identify the active regions of the brain, the periodogram method which is obtained by Fourier analysis to help discriminate between groups and Simple Moving Average to
eliminate some of the existing noise in the data. It was developed two functions in the
software R, for the realization of training tasks and classification. Also, it was proposed
two weights systems and a summarized measure to help on deciding in classification of
groups. The application of these techniques, weights and the summarized measure in
the classier, showed quite satisfactory results, where the best results were an average
rate of 95.31% to visual stimuli data, 100% of correct classification for epilepsy data
and rates of 91.22% and 96.89% to object motion data for two subjects.