The increase in Autism Spectrum Disorder (ASD) prevalence estimates over the last decades has driven a quest to develop new forms of rehabilitation that can be accessible to a larger part of this population. These rehabilitation approaches often take the form of computer games that are blind to the user's emotional state, which compromises their efficacy. In this study, a set of physiological signals were acquired in simultaneous with functional Magnetic Resonance Imaging (fMRI) with the future prospect of combining both kinds of data to create models capable of assessing the true emotional state of their users based on physiological response as a measure of autonomic nervous system, having as ground truth the activity of targeted brain regions. This paper describes an initial approach, focusing on the information contained on the physiological signals alone. A total of 35 features were extracted from biosignals' segments and subsequently used for automatic classification of arousal state (High Arousal vs. Low Arousal). The suboptimal results, although some extracted features present statistically significant differences, underline the challenging nature of our proposal and the added obstacles of recording physiological signals in the magnetic resonance environment. Further exploration of the measured signals is needed to gather a bigger number of discriminative features that can improve classification outcomes.