Made available in DSpace on 2018-08-02T00:00:51Z (GMT). No. of bitstreams: 1 tese_3573_DissertacaoMestradoAlexBrandaoRossow.pdf: 1877924 bytes, checksum: ea3ef5771395946a600614df83e029b9 (MD5) Previous issue date: 2010-12-07 / Esta pesquisa propõe o estudo dos sinais do eletroencefalogreama (EEG) e a elaboração de um sistema automatizado para detecção dos vários estágios de consciência dos indivíduos durante o trabalho, operação de equipamentos ou sob condições de intervenção cirúrgica. O sistema irá apontar se o indivíduo está acordado, sonolento, em sono leve ou em sono profundo, permitindo a intervenção em situações de risco, como na operação de máquinas e veículos, em que se deseja que o indivíduo permaneça em alerta, ou em situações médicas, onde é necessário que o paciente se mantenha dormindo durante o processo cirúrgico.

Made available in DSpace on 2018-08-02T00:01:59Z (GMT). No. of bitstreams: 1 tese_3870_Tese_Alessandro_Botti_Benevides_600dpi.pdf: 13357880 bytes, checksum: d7eb3ecdca23180cb3af92d0ea795d0e (MD5) Previous issue date: 2013-08-30 / This present research proposes a Brain-Computer Interface (BCI) architecture adapted to motor mental tasks and music imagery. For that purpose the statistical properties of the electroencephalographic signal (EEG) were studied, such as its probability distribution function, stationarity, correlation and signal-to-noise ratio (SNR), in order to obtain a minimal empirical and well-founded parameter system for online classification. Stationarity tests were used to estimate the length of the time windows and a minimum length of 1.28 s was obtained. Four algorithms for artifact reduction were tested: threshold analysis, EEG filtering and two Independent Component Analysis (ICA) algorithms. This analysis concluded that the algorithm fastICA is suitable for online artifact removal. The feature extraction used the Power Spectral Density (PSD) and three methods were tested for automatic selection of features in order to have a training step independent of the mental task paradigm, with the best performance obtained with the Kullback-Leibler symmetric divergence method. For the classification, the Linear Discriminant Analysis (LDA) was used and a step of reclassification is suggested. A study of four motor mental tasks and a non-motor related mental task is performed by comparing their periodograms, Event-Related desynchronization/synchronization (ERD/ERS) and SNR. The mental tasks are the imagination of either movement of right and left hands, both feet, rotation of a cube and sound imagery. The EEG SNR was estimated by a comparison with the correlation between the ongoing average and the final ERD/ERS curve, in which we concluded that the mental task of sound imagery would need approximately five times more epochs than the motor-related mental tasks. The ERD/ERS could be measured even for frequencies near 100 Hz, but in absolute amplitudes, the energy variation at 100 Hz was one thousand times smaller than for 10 Hz, which implies that there is a small probability of online detection for BCI applications in high frequency. Thus, most of the usable information for online processing and BCIs corresponds to the α/µ band (low frequency). Finally, the ERD/ERS scalp maps show that the main difference between the sound imagery task and the motor-related mentaltasks is the absence of ERD at the µ band, in the central electrodes, and the presence of ERD at the αband in the temporal and lateral-frontal electrodes, which correspond tothe auditory cortex, the Wernickes area and the Brocas area.