Photosensitivity is a neurological condition in which the brain produces epileptiform reactions to visual stimuli known as Photoparoxysmal Responses (PPR). These events are typically diagnosed through Intermittent Photic Stimulation (IPS) while monitoring brain signals with electroencephalography (EEG). Manual analysis of PPR is time-consuming and subjective, which motivates the development of automated detection methods. In this work, we propose an unsupervised anomaly detection (AD) approach using a Variational Autoencoder (VAE) trained exclusively on normal EEG segments from non-photosensitive patients. The model is evaluated on EEG recordings from photosensitive patients to identify PPR activity as deviations from normal patterns. In previous research, this VAE model outperformed other unsupervised AD models in the literature for this task; however, it generated a large number of False Positives that were later confirmed as EEG anomalies that were not labelled. This research represents the first step in an EEG anomaly detection and multi-classification framework, aiming not only to detect PPR but to automatically label all Positive instances as the different types of anomalous EEG patterns in future work. Results reveal that the model performed well, reaching 83% Accuracy, Sensitivity and Specificity, despite producing numerous False Positives as expected due to the lack of labels. This research is carried out with real EEG recordings gathered at Cabueñes University Hospital, Spain.