Understanding the neurophysiological mechanisms underlying curiosity and therefore being able to identify the curiosity level of a person, would provide useful information for researchers and designers in numerous fields such as neuroscience, psychology, and computer science. A first step to uncovering the neural correlates of curiosity is to collect neurophysiological signals during states of curiosity, in order to develop signal processing and machine learning (ML) tools to recognize the curious states from the non-curious ones. Thus, we ran an experiment in which we used electroencephalography (EEG) to measure the brain activity of participants as they were induced into states of curiosity, using trivia question and answer chains. We used two ML algorithms, i.e. Filter Bank Common Spatial Pattern (FBCSP) coupled with a Linear Discriminant Algorithm (LDA), as well as a Filter Bank Tangent Space Classifier (FBTSC), to classify the curious EEG signals from the non-curious ones. Global results indicate that both algorithms obtained better performances in the 3-to-5s time windows, suggesting an optimal time window length of 4 seconds (63.09% classification accuracy for the FBTSC, 60.93% classification accuracy for the FBCSP+LDA) to go towards curiosity states estimation based on EEG signals.