Ulster University Logo

Virtual Reality, Graphics and mVEP Classification

Beveridge, Ryan, Wilson, Shane and Coyle, Damien (2016) Virtual Reality, Graphics and mVEP Classification. In: The 6th International Brain-Computer Interface Meeting, Asilomar, California. Verlag der TU Graz, Graz University of Technology. 1 pp. [Conference contribution]

[img] Text - Published Version
[img] Text - Supplemental Material
Indefinitely restricted to Repository staff only.


URL: http://castor.tugraz.at/doku/BCIMeeting2016/paper_125.pdf

DOI: 10.3217/978-3-85125-467-9-125


Brain computer interfaces (BCIs) have often been interfaced with video games however the impact that video games graphics complexity has on brain-computer games interaction (BCGI) performance has not been studied. Additionally, with more advanced visual displays such as the Oculus Rift Virtual Reality (VR) headset there is a need to investigate any (dis)advantages these variables may have on BCGI. This is particularly relevant for visual evoked potential (VEP) based paradigms where visual distractions may have an impact on the reliability of the EP. In this study we utilized an Oculus Rift headset as a visual display to present a motion-onset VEP (mVEP) controlled car racing game and compared the offline mVEP classification performance with the same game presented on a standard 22 inch LCD computer screen. We also compared two different levels of graphical complexity and background styles for the mVEP evoking stimuli. mVEPs are elicited by the sudden, brief motion (lasting 140ms) of an attended target/stimulus and consists of a negative peak around 200ms (P2) after the evoked stimulus, followed by a positive peak at around 300ms (P3). mVEP stimuli are more elegant as they are motion related, do not require long training periods and are less visually fatiguing than other VEP stimuli.

Item Type:Conference contribution (Paper)
Keywords:motion onset visual evoked potentials, oculus rift, virtual reality, neurogaming, brain-computer interface (BCI), electroencephalography (EEG),
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:35126
Deposited By: Prof Damien Coyle
Deposited On:21 Feb 2017 15:28
Last Modified:21 Feb 2017 15:28

Repository Staff Only: item control page