Restriction lift date: 2026-09-30
Interactive EEG visualisation in Virtual Reality: design and implementation
Loading...
Files
Date
2025
Authors
Creed, Adam
Journal Title
Journal ISSN
Volume Title
Publisher
University College Cork
Published Version
Abstract
Electroencephalography (EEG) has long been a cornerstone in both neuroscience research and clinical diagnostics, providing invaluable insights into the electrical activity of the brain. EEG is the gold standard to detect and analyse brain pathologies such as seizures. An early detection and diagnosis of seizures is key in an effective treatment and decrease in morbidity and mortality. However, neonatal seizures are difficult to detect through clinical signs and even through EEG. The standard method of analysis of neonatal EEG is through visualisation on conventional two dimensional (2D) displays. Despite its importance, interpreting the complex and often nuanced signals produced by EEG remains a significant challenge, particularly when viewed on conventional 2D displays. This limitation can be particularly evident for clinicians who may lack extensive experience in EEG analysis. As the demand for more accurate and accessible diagnostic tools grows, the need for more intuitive methods of data visualisation becomes increasingly apparent. This thesis addresses these challenges by presenting the design, development, and comprehensive evaluation of an innovative neonatal EEG visualisation platform within a Virtual Reality (VR) environment, developed using Unity. The platform reimagines how EEG data can be presented, leveraging the immersive capabilities of VR to offer a fully three-dimensional (3D) space where users can interact with and explore brain activity data in real time. By moving beyond the constraints of 2D screens, this approach provides a more natural, immersive, and intuitive framework for both novice and expert clinicians alike.
Recently, another method of analysis through sonification of EEG was proposed to speed up analysis of EEG. This allows users to hear the brain’s electrical activity as a dynamic auditory experience. This novel sonification technique not only provides an additional sensory modality for interpreting EEG data when used complementary with visualisation but also enhances the users’ ability to detect patterns and anomalies in the brain’s electrical signals that may be overlooked visually. Therefore, it was added to this platform. Furthermore, the platform integrates an AI-driven seizure detection algorithm.
This feature maps detected seizure events onto a 3D brain model, offering clinicians a more spatially accurate representation of potential seizure zones. The ability to visualise these detections in a 3D context is expected to improve clinical decision-making.
To validate the effectiveness of this VR-based EEG platform, a series of user evaluations was conducted. Participants, including both clinicians with EEG knowledge and those without prior experience, interacted with the system and provided feedback on its usability and functionality. The results were overwhelmingly positive, with the platform achieving a high System Usability Scale (SUS) score of 83, indicating strong user satisfaction. Participants also completed the NASA Task Load Index (NASA-TLX), with an average score of 36.65, reflecting a low perceived cognitive workload during the interaction. This suggests that the platform not only offers a rich and engaging user experience but also minimises the mental effort required to interpret complex EEG data.
In addition to its intuitive user interface, the platform was rigorously tested across a range of hardware configurations, from high-end VR systems to more affordable, lowerend devices. It performed consistently well, demonstrating its potential for widespread adoption in diverse clinical environments, from cutting-edge research facilities to more resource-constrained healthcare settings.
Description
Keywords
Virtual Reality , EEG , Sonification
Citation
Creed, A. 2025. Interactive EEG visualisation in Virtual Reality: design and implementation. MRes Thesis, University College Cork.