Callejas J.D.C.2020-03-262020-03-2620182018 IEEE ANDESCON, ANDESCON 2018 - Conference Proceedings9781538683729https://hdl.handle.net/20.500.12585/9204Brain activity during perception and recognition of faces have been studied by researchers with the purpose to develop brain-computer interfaces and to study neurological disorders. In this paper, we analyzed evoked potentials as neurophysiological indicators and developed a model based on signal processing and machine learning techniques to find descriptive patterns that allow the differentiation of familiar and unfamiliar faces. We considered wave components such as P1, N170, N250, P300, and N400 to describe the events. Morphological analysis and wavelet transform were used for the feature extraction stage, and support vector machines and binomial logistic regression were evaluated for the classification stage. The best classification results were obtained with the morphological characteristics, where the highest classification accuracy was 80% on average. © 2018 IEEE.Recurso electrónicoapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/conferenceObject10.1109/ANDESCON.2018.8564591ElectroencephalographyEvoked potentialsFace recognitionMachine learningWavelet transformArtificial intelligenceBioelectric potentialsBiomedical signal processingBrainBrain computer interfaceElectroencephalographyElectrophysiologyLearning systemsNeurophysiologyWavelet transformsBinomial logistic regressionsClassification accuracyClassification resultsFeature extraction stagesMachine learning techniquesMorphological analysisMorphological characteristicPerception and recognitionFace recognitioninfo:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 InternacionalUniversidad Tecnológica de BolívarRepositorio UTB5720552886957210822856