Abstract
A phobia is a human fear of things that are sometimes very simple for some people. One of them is Trypophobia which is a fear of visualizing small holes. The effect of the trypophobia effect can we analyze his brain waves using an Electroencephalograph. In this study, a system was developed to classify a person’s condition without Trypophobia (normal) and the condition of a person with Trypophobia based on analysis of alpha and beta EEG signals. In this study, the Artificial Neural Network (ANN) used for classifying conditions. Discrete Wavelet Transform (DWT) is used to reduce the raw dimensions of EEG signals and retrieve signal features. The test results show that the best performance obtained in beta signals which have the highest diagnostic parameter accuracy, namely Maximum, Standard Deviation and Variance with 100% accuracy, with computation time 0.027 and 0.037 seconds. While for alpha signals obtained with Variance and Interquartile Range parameters of 96.42% with a time of 0.03 and 0.032 seconds.
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