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Title: An assessment of factors influencing neurogaming with motion-onset visual evoked potentials (mVEPs)
Author: Beveridge, Ryan
ISNI:       0000 0004 6500 8484
Awarding Body: Ulster University
Current Institution: Ulster University
Date of Award: 2018
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Brain-computer interface (BCI) technology offers movement-independent control of computer applications by translating cortical activity into semantic control signals for a computer to execute. One prominent application of BCI technology is brain-computer games interfacing (BCGI) or neurogaming. This thesis aimed to advance the field of neurogaming and is an account of the work conducted whilst investigating the feasibility of employing motion-onset visual evoked potentials (mVEPs) for control in a range of neurogames and the factors that influence performance when employing such a control strategy. mVEPs manifest near the visual cortex when motion-related stimuli are attended to visually and therefore are likely to be elicited naturally by games graphics scenes and the motions of in-game objects. There are limited studies investigating the potential for mixing games graphics with visual motion-based stimuli to elicit mVEPs for control in games. This thesis addresses this lacuna and improves our understanding of the factors that influence neurogaming with mVEPs. Firstly, participants were presented with mVEP-inducing stimuli overlaid on games graphics of varying complexity. Offline analysis of the EEG indicated that there was some correlation between graphical complexity and mVEP detection performance but the differences were insignificant for moderate variations in graphical complexity. Another offline study involved mVEP stimuli mixed with five different commercially available video games - each representing different graphical complexities, genre, and generation of gaming console. A 3D fast-paced car-racing game consistently provided the greatest mVEP detection accuracies. To validate the use of a virtual reality (VR)-based display modality, two different game level presentations, based on the car-racing genre were studied - one with rudimentary and one with highly detailed graphics. Offline results indicated that mVEP detection accuracy provided by the Oculus Rift VR headset did not differ to an LCD computer display, demonstrating the possibility of employing contemporary display technologies in neurogaming. Once we established that mVEPs could be detected with graphics of varying complexity and that perhaps the car-racing genre is best suited for mVEP-based control, a series of online control experiments with a mVEP-controlled 3D car-racing game were conducted comparing the performance of adults to teenagers, a relatively understudied age group in neurogaming. We investigated user performance based on different lap speeds dictated by the number of event-related potentials (ERPs) averaged to make a game control decision. Our findings indicate that adult participants outperformed their teenage counterparts and that mVEP detection is robust to variations in the setup of the signal processing and system calibration. In summary, this thesis has implications for BCI control strategies involving mVEPs, gameplay quality, speed of control, performance assessment and calibrating mVEP-based BCIs. A broad range of users, including teenagers, have been evaluated in a mVEP-based neurogaming study for the first time.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available