Lynda Gerry
Barrett Ens
ABSTRACT
This paper presents the ongoing development of a proof-of-concept, adaptive system that uses a neurocognitive signal to facilitate efficient performance in a Virtual Reality visual search task. The Levity system measures and interactively adjusts the display of a visual array during a visual search task based on the user's level of cognitive load, measured with a 16-channel EEG device. Future developments will validate the system and evaluate its ability to improve search efficiency by detecting and adapting to a user's cognitive demands.
Supplemental Material
- Jackson Beatty. 1982. Task-evoked pupillary responses, processing load, and the structure of processing resources. Psychological Bulletin 91, 2: 276--292.Google Scholar
Cross Ref
- Roland Brunken, Jan L. Plass, and Detlev Leutner. 2003. Direct Measurement of Cognitive Load in Multimedia Learning. Educational Psychologist 38, 1: 53--61.Google ScholarCross Ref
- Erik Van der Burg, Christian N. L. Olivers, Adelbert W. Bronkhorst, and Jan Theeuwes. 2008. Pip and Pop: Nonspatial Auditory Signals Improve Spatial Visual Search. Journal of Experimental Psychology: Human Perception and Performance 34, 5: 1053--1065.Google ScholarCross Ref
- B. H. Cho, J. M. Lee, J. H. Ku, D. P. Jang, J. S. Kim, I. Y. Kim, J. H. Lee, and S. I. Kim. 2002. Attention Enhancement System using virtual reality and EEG biofeedback. July 2014: 156--163. Google ScholarDigital Library
- J. Duncan and G. W. Humphreys. 1989. Visual-Search and Stimulus Similarity. Psychological Review 96, 3: 433--458.Google ScholarCross Ref
- Alan Gevins and Michael E. Smith. 2000. Neurophysiological measures of working memory and individual differences in cognitive ability and cognitive style. Cerebral cortex (New York, N.Y.: 1991) 10, 9: 829--839.Google Scholar
- W. Klimesch, H. Schimke, and G. Pfurtscheller. 1993. Alpha frequency, cognitive load and memory performance. Brain Topography 5, 3: 241--251.Google ScholarCross Ref
- Wolfgang Klimesch. 1999. EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. Brain Research Reviews 29, 2-3: 169--195.Google ScholarCross Ref
- A. Lecuyer, F. Lotte, R. B. Reilly, R. Leeb, M. Hirose, and M. Slater. 2008. Brain-Computer Interfaces, Virtual Reality, and Videogames. Computer 41, 10: 66--72. Google ScholarDigital Library
- Robert Leeb, Doron Friedman, Gernot R. Müller-Putz, Reinhold Scherer, Mel Slater, and Gert Pfurtscheller. 2007. Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: A case study with a tetraplegic. Computational Intelligence and Neuroscience 2007. Google ScholarDigital Library
- Fred G. W. C. Paas, Jeroen J. G. van Merriënboer, and Jos J. Adam. 1994. Measurement of Cognitive Load in Instructional Research. Perceptual and Motor Skills 79, 1: 419--430.Google ScholarCross Ref
- Paul Sauseng and Wolfgang Klimesch. 2008. What does phase information of oscillatory brain activity tell us about cognitive processes? Neuroscience and Biobehavioral Reviews 32, 5: 1001--1013.Google ScholarCross Ref
- Richard M. Shiffrin and Walter Schneider. 1977. Controlled and automatic human information processing: II. Perceptual learning, automatic attending and a general theory. Psychological Review 84, 2: 127--190.Google ScholarCross Ref
- J. Sweller. 1988. Cognitive load during problem solving: Effects on learning. Cognitive Science 12, 2: 257--285.Google ScholarCross Ref
- Karl Halvor Teigen. 1994. Yerkes-Dodson: A Law for All Seasons. Theory & Psychology 4, 4: 525--547.Google ScholarCross Ref
- Junichi Tsurukawa, Mohammed Al-Sada, and Tatsuo Nakajima. 2015. Filtering Visual Information for Reducing Visual Cognitive Load. Proceedings of the 2015 ACM International Symposium on Wearable Computers: 33--36. Google ScholarDigital Library
- Athanasios Vourvopoulos, Sergi Bermudez i Badia, and Fotis Liarokapis. 2017. EEG correlates of video game experience and user profile in motor-imagery-based brain-computer interaction. Visual Computer 33, 4: 533--546. Google ScholarDigital Library
- Jonathan R. Wolpaw, Niels Birbaumer, Dennis J. McFarland, Gert Pfurtscheller, and Theresa M. Vaughan. 2002. Brain-computer interfaces for communication and control. Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology 113, 6: 767--91.Google Scholar
- Qi Bin Zhao, Li Qing Zhang, and Andrzej Cichocki. 2009. EEG-based asynchronous BCI control of a car in 3D virtual reality environments. Chinese Science Bulletin 54, 1: 78--87.Google ScholarCross Ref
Index Terms
- Levity: A Virtual Reality System that Responds to Cognitive Load
Recommendations
In AI We Trust: Investigating the Relationship between Biosignals, Trust and Cognitive Load in VR
VRST '19: Proceedings of the 25th ACM Symposium on Virtual Reality Software and TechnologyHuman trust is a psycho-physiological state that is difficult to measure, yet is becoming increasingly important for the design of human-computer interactions. This paper explores if human trust can be measured using physiological measures when ...
Effectiveness of commodity BCI devices as means to control an immersive virtual environment
SUI '13: Proceedings of the 1st symposium on Spatial user interactionThis poster focuses on research investigating the control of an immersive virtual environment using the Emotiv EPOC, a consumer-grade brain computer interface. The primary emphasis of the work is to determine the feasibility of the Emotiv EPOC at ...
Effects of virtual reality display types on the brain computer interface system
UAHCI'07: Proceedings of the 4th international conference on Universal access in human-computer interaction: ambient interactionThis paper presents the study of evaluating VR display types on Brain Computer Interface (BCI) performance. In this study, a configurable virtual reality BCI system was used for users to control the virtual environment to execute the ubiquitous ...
Comments