Fernando Moncada Martins

arXiv2026

VR-IPS: Virtual Reality Tool for Photic Stimulation in Photosensitive Diagnosis

Photosensitivity is a neurological condition in which the brain generates epileptiform activity in response to visual stimuli. The standardized clinical diagnosis procedure, named Intermitent Photic Stimulation (IPS), involves exposing patients to a white flashing light at different frequencies to provoke this reaction. However, clinical neurophysiologists report that this protocol is insufficient, leading to underdiagnosis. VR-IPS is a flexible visual stimulation tool that extends conventional flashing-light stimulation by incorporating configurable color lights, static and oscillatory motion patterns, and photoprovocative videos. The system comprises a stimulation module that can run on Virtual Reality headsets or on standard monitors, together with a control module that allows clinicians to create, select, and configure visual stimulus, while controlling the whole stimulation procedure.

q-bio.NC↗

arXiv2025

Inception networks, Data Augmentation and Transfer Learning in EEG-based photosensitivity diagnosis

Photosensitivity refers to a neurophysiological condition in which the brain generates epileptic discharges known as Photoparoxysmal Responses (PPR) in response to light flashes.In severe cases, these PPR can lead to epileptic seizures. The standardized diagnostic procedure for this condition is called Intermittent Photic Stimulation. During this procedure, the patient is exposed to a flashing light, aiming to trigger these epileptic reactions while preventing their full development. Meanwhile, brain activity is monitored using Electroencephalography, which is visually analyzed by clinical staff to identify these responses. Hence, the automatic detection of PPR becomes a highly unbalanced problem that has been barely studied in the literature due to photosensitivity's low prevalence. This research tackles this problem and proposes using Inception-based Deep Learning (DL) neural networks that, together with transfer learning, are trained in epilepsy seizure detection and tuned in the PPR automatic detection task. A Data Augmentation (DA) technique is also applied to balance the available data set, evaluating its effects on the DL models. The proposal outperformed state-of-the-art solutions in the literature, achieving higher ratios on standard performance metrics, and with DA significantly improving the Sensitivity without affecting Accuracy and Specificity. This project is currently being developed with patients from Burgos University Hospital, Spain

eess.SP↗