Ciclo de seminários do Programa de Pós-Graduação em Matemática Aplicada e Estatística da UFRN - 2017

Título: Support vector machine classifier with Fourier analysis applied to  EEG and EMG data

Palestrante: Prof. Dr. Andre Luís Santos de Pinho - UFRN

Quando: 21 de setembro de 2017, quinta-feira, às 14:00h.

Onde: Sala de Seminários da Estatística – CCET- UFRN

Resumo.  The classier support vector machine is used in several problems in  various areas of knowledge. Basically the method used in this classier is  to nd the hyperplane that maximizes the distance between the groups, to  increase the generalization of the classier. In this work, we treated some  problems of binary classication of data obtainedby 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 classication. Also, it was proposed two weights systems and a summarized measure to help on deciding in classication 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  classication for epilepsy data and rates of 91.22% and 96.89% to object motion data for two subjects.