Improving Online Accessibility for Individuals with Photosensitive Epilepsy

Content with flashes, bright colors, and repeated patterns can cause seizures and migraines when viewed by people with photosensitivity. Exposure to seizure-inducing content is a serious risk in online environments, as evidenced by documented instances of people with photosensitivity being exposed to seizure-inducing material while playing video games or using social media.My thesis focuses on improving online safety and accessibility for people with photosensitivity by measuring the prevalence of seizure-inducing content online, developing new tools for detecting seizure-inducing content, and constructing a broad framework for protecting against seizure-inducing content at the level of content creators, platforms, and content consumers. Through this work, I hope to help build a better understanding of the current state of photosensitive risk online and contribute new solutions for mitigating seizure-inducing content with minimal adverse effects on the browsing experience for users with photosensitivity.

FNS-parameterization of human magnetoencephalograms for the diagnosis of photosensitive epilepsy

This work presents the results of parameterization of magnetoencephalogram signals from healthy subjects and a patient with photosensitive epilepsy. Diagnostic criteria were established during the extraction of resonant and high-frequency (chaotic) components of the initial time signals. It is shown that an increase in the intensity of the chaotic components of the studied signals in the high-frequency region leads to a violation of cross-correlation relationships and a decrease in the level of manifestation of frequency-phase synchronization. The discovered signs of photosensitive epilepsy will contribute to the development of new methods for the diagnosis and medical control of this disease based on Flicker-Noise Spectroscopy.

Detecting and Defending Against Seizure-Inducing GIFs in Social Media

Despite recent improvements in online accessibility, the Internet remains an inhospitable place for users with photosensitive epilepsy, a chronic condition in which certain light stimuli can trigger seizures and even lead to death in extreme cases. In this paper, we explore how current risk detection systems have allowed attackers to take advantage of design oversights and target vulnerable users with photosensitivity on popular social media platforms. Through interviews with photosensitive individuals and a critical review of existing systems, we constructed design requirements for consumer-driven protective systems and developed a prototype browser ex-tension for actively detecting and disarming potentially seizure-inducing GIFs and videos. We validate our system with a comprehensive dataset of simulated GIFs and GIFs collected from social media. Finally, we conduct a novel quantitative analysis of the prevalence of seizure-inducing GIFs across popular social mediaplatforms and contribute recommendations for improving online accessibility for individuals with photosensitivity. All study materialsare available at https://osf.io/5a3dy/.

Warning Light Flash Frequency as a Method for Visual Communication to Drivers

Service vehicles use flashing warning lights to indicate their presence to approaching drivers. Present standards offer ranges of flash frequencies to enhance conspicuity and avoid potential risks of photosensitive epilepsy or other issues. But, in practice, the flash frequency is not varied in specific situations. Previous studies have indicated that people interpret faster flash frequencies as more “urgent” than slower flash frequencies. Building on these findings, a laboratory study was conducted to identify whether drivers might be able to use cues from the frequency of flashing warning lights to anticipate how a service vehicle might behave in a work zone or other incident scene. The results suggest that even if they are not taught about the interpretation of different flash frequencies, drivers can differentiate between 1 Hz and 4 Hz flashing lights and learn to make accurate predictions about their meaning. The results also indicate that there are no reliable differences between 1 Hz and 4 Hz flashing in relation to a driver’s ability to detect when a service vehicle has begun to move. Based on the results, a preliminary suggestion is made to use lights flashing at 1 Hz when a service vehicle is moving forward, and 4 Hz when it is traveling in reverse.

Ethical Considerations of Photosensitive Epilepsy in Mixed Reality

As public interest in virtual and augmented reality increases, so do the risks faced by users with photosensitive epilepsy, a neurological condition where seizures are triggered by specific kinds of light stimuli. Most research on photosensitive epilepsy focuses on user interaction with television sets and does not necessarily transfer to other methods of interaction. Very little research has been done examining mixed reality from the perspective of photosensitive users and understanding the additional risks posed by new forms of immersive technology. Examples of hackers targeting people with photosensitive epilepsy through social media serve as a wake-up call about the possibility of similar malicious attacks in mixed reality. In this paper we draw from photosensitive epilepsy research to create recommendations for simple steps mixed reality developers can take to minimize photosensitive risk in their systems in addition to issuing a broader call for further research into understanding photosensitive epilepsy in the context of mixed reality.

Generating Seizure-Inducing Sequences with Interactive Visualizations

Interactive visualizations are often built to draw the eye towards pertinent information with attention-grabbing pops of color and patterns. These techniques, though helpful in engaging the average user and nudging them towards important information, can be harmful to users with photosensitive epilepsy, who may experience seizures when exposed to content with flashes, transitions to and from saturated red, or repeated patterns. In this paper, we explore three case studies of interactive visualizations created without malicious intent yet capable of producing seizure-inducing sequences through interaction alone. Based on these case studies as well as relevant related literature, we contribute a set of simple recommendations to help visualization designers and developers avoid accidentally creating interactive visualizations with the potential to cause seizures.