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dc.rights.license http://creativecommons.org/licenses/by-nc-nd/4.0 es_ES
dc.contributor Juvenal Rodríguez Reséndiz es_ES
dc.creator Cesar Javier Ortiz Echeverri es_ES
dc.date 2020-01-30
dc.date.accessioned 2020-01-31T16:21:53Z
dc.date.available 2020-01-31T16:21:53Z
dc.date.issued 2020-01-30
dc.identifier.uri http://ri-ng.uaq.mx/handle/123456789/2011
dc.description Brain-Computer Interfaces (BCI) are systems that provide an alternative communication between the human brain and a computer, where electroencephalography (EEG) is the the non-invasive and most viable way to obtain the electrophysiological activity. However, the EEG register presents several important challenges for both the instrumentation and the signal processing techniques involved in a BCI implementation. Some of the most relevant drawbacks are due to the low signal-to-noise ratio, the presence of undesirable signals such as ocular, cardiac, and muscular activity, as well as a low spatial resolution due to the distance and high impedance between the sources and the location of the electrodes. Therefore, the raw of EEG signals have very low amplitude, very low signal-to-noise ratio, and considerable noise contamination. Digital signal processing, and machine learning have been included in the preprocessing, feature extraction and classication stages of BCI systems in order to improve the signal-to-noise-ratio and hence, increase their effciency. The rst part of the present dissertation consists of comparing the performance of different preprocessing algorithms to estimate the original sources from the EEG registers; particularly, the preprocessing was made using Blind Source Separation (BSS) algorithms. This kind of spatial lters are based on Second Order Statistic (SOS) or High Order Statistic (HOS) information. Most representative BSS algorithms are (SOBI, SOBIRO, fastICA, and Infomax) were compared using semi-simulated sources, using the Pearson's correlation coecient and the Wavelet Coherence (WC) as metrics. This analysis was conducted in the electrophysiological bands. On the other hand, an analysis of dierent descriptors in time and frequency domain was performed for the extraction of relevant features was made in order to nd the most relevant information and thus reduce the dimensionality of input data. In this stage, the used classier was an Multilayer Perceptron (MLP). Finally, new method built from the combination of a Blind Source Separation (BSS) to obtain estimated independent components, a 2D representation of these component signals using the ContinuousWavelet Transform (CWT), and a classication stage using a Convolutional Neural Network (CNN) has been proposed. A criterion based on the spectral correlation with a Movement Related Independent Component (MRIC) is used to sort the estimated sources by BSS, thus reducing the spatial variance. The experimental results of 94.21% using a k-fold cross validation are competitive with techniques recently reported in the state-of-the-art. es_ES
dc.format Adobe PDF es_ES
dc.language.iso spa es_ES
dc.relation.requires Si es_ES
dc.rights Acceso Abierto es_ES
dc.subject Interfaces Cerebro-Computadora es_ES
dc.subject Redes Neuronales es_ES
dc.subject Separación de Fuentes Ciegas es_ES
dc.subject.classification INGENIERÍA Y TECNOLOGÍA es_ES
dc.title Procesamiento de Señales de Electroencefalograma usando Redes Neuronales para aplicaciones en Sistemas BCI es_ES
dc.type Tesis de doctorado es_ES
dc.creator.tid curp es_ES
dc.contributor.tid curp es_ES
dc.creator.identificador OIEC831207HNERCS06 es_ES
dc.contributor.identificador RORJ840929HQTDSV02 es_ES
dc.contributor.role Director es_ES
dc.degree.name Doctorado en Ciencias de la Computación es_ES
dc.degree.department Facultad de Informática es_ES
dc.degree.level Doctorado es_ES


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