Uma contribuição ao estudo de sinais de EEG para avaliar estados emocionais e mentais de crianças com autismo na interação com robô móvel

• Autor(a) principal: Goulart, Christiane Mara
• Data de Publicação: 2015
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da Universidade Federal do Espírito Santo (riUfes)
• Texto Completo: http://repositorio.ufes.br/handle/10/1346
• topic: Autism Spectrum Disorder (ASD); Mobile robotics; Biotecnologia; Autismo; Emoções; Robótica; 61

Autism Spectrum Disorder (ASD) is characterized by a series of cognitive and neurobehavioral disorders and its global prevalence is estimated at 1 child with ASD per 160 children typically developed (TD). Individuals with ASD have difficulty in interpreting others' emotions and expressing feelings. The emotions may be associated to the manifestation of physiological signals, and, among them, the brain signals have been much discussed. The detection of brain signals of children with ASD can be beneficial to clarify their emotions and expressions. Currently, many researches integrate robotics to pedagogical treatment of ASD, through the interaction with children with this disorder, stimulating social skills such as the ability of imitation and communication. The evaluation of mental states of children with ASD during their interaction with a mobile robot is promising and innovative. Therefore, the goals of this study were to capture brain signals of children with ASD and TD, as control group, for the study of their emotional states and to evaluate their mental states during the interaction with a mobile robot, and evaluating also the interaction of these children with the robot, using quantitative scales. The technique of brain signals recording chosen was lectroencephalography (EEG), which uses electrodes placed noninvasively and painless on the scalp. The methods to evaluate the efficiency of the use of the robotics in this interaction were based on two quantitative international scales: Goal Attainment Scaling (GAS) and System Usability Scale (SUS). Results showed that, using EEG, it was possible to classify emotional states of children with ASD and TD and analyze brain activity during the start of the interaction with the robot, through the alpha and beta rhythms. With GAS and SUS scales, it was found that the robot can be considered a potential therapeutic tool for children with ASD.