Implementation of artificial intelligence algorithm for EEG signals classification.

Iván Alejandro García Amaya

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dc.rights.license	http://creativecommons.org/licenses/by-nc-nd/4.0	es_ES
dc.contributor	Marco Antonio Aceves Fernandez	es_ES
dc.creator	Iván Alejandro García Amaya	es_ES
dc.date	2021-11-03
dc.date.accessioned	2021-11-09T18:19:17Z
dc.date.available	2021-11-09T18:19:17Z
dc.date.issued	2021-11-03
dc.identifier.uri	http://ri-ng.uaq.mx/handle/123456789/3220
dc.description	Attention Deficit Hyperactivity Disorder is one of the problems that affect a considerable part of society, in this research work a couple of methodologies are proposed to address this problem through the classifications of electroencephalogram signals. The first methodology consists of filtering the electroencephalogram signals to remove the greatest amount of noise and to extract the beta band. Then it is necessary to extract the Absolute Power of the electroencephalogram signals from the selected channels (Cz, C3, C4, Fz, Pz). The Absolute Power generates a smaller data set with the help of a Genetic Algorithm which selects the most representative attributes to be able to represent them in 2D images. In this case, with the help of the Continuous Wavelet Transform technique, a Power Spectrum represented in 2D images is obtained. The three Deep Learning Models that are implemented are ResNet-152, the ResNeXt-101 and the GoogLeNet. Similar results are observed with the implementation of these models; with the ResNet-152 model an accuracy of 86.6% is shown as a result, with the ResNeXt-101 model shows an accuracy of 83.92% and with the GoogLeNet model an accuracy of 85.74 %. The second methodology for the approach to the classification of people diagnosed with Attention Deficit and Hyperactivity Disorder and people of Control making use of the electroencephalogram, proposes a filtering phase first where the noise from the signals is removed, while extracting the beta band and five different measurs are extracted: Standard Deviation, Variance, Entropy, Absolute Power and Relative Power. Each of these measurs provides relevant information. Two algorithms are implemented which are Logistic Regression and the Maximum Likelihood, these models are trained with 70% of the data set and 30% are used for validation that is randomly selected, this process is repeated several times and the best results obtained are 80.30% of accuracy in the Logistic Regression model and 84.40% in the Maximum Likelihood model, greater extraction of characteristics translates into a greater amount of information, the correct extraction of information allows us to implement algorithms such as Logistic Regression and Maximum likelihood.	es_ES
dc.format	Adobe PDF	es_ES
dc.language.iso	spa	es_ES
dc.relation.requires	No	es_ES
dc.rights	Acceso Abierto	es_ES
dc.subject	ADHD	es_ES
dc.subject	EEG signals	es_ES
dc.subject	Machine Learning	es_ES
dc.subject	Deep Learning	es_ES
dc.subject	TDAH	es_ES
dc.subject.classification	INGENIERÍA Y TECNOLOGÍA	es_ES
dc.title	Implementation of artificial intelligence algorithm for EEG signals classification.	es_ES
dc.type	Tesis de maestría	es_ES
dc.creator.tid	curp	es_ES
dc.contributor.tid	curp	es_ES
dc.creator.identificador	GAAI940501HDGRMV03	es_ES
dc.contributor.identificador	AEFM780704HMCCRR09	es_ES
dc.contributor.role	Director	es_ES
dc.degree.name	Maestría en Ciencias en Inteligencia Artificial	es_ES
dc.degree.department	Facultad de Ingeniería	es_ES
dc.degree.level	Maestría	es_ES