Modelling Learning in an Educational Game
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Evolutionary Brain Computer Interfaces
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Inferring cognition from fMRI brain images
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Measuring interest in linear single player FPS games
ACM SIGGRAPH ASIA 2010 Sketches
Multi-modal biometric emotion recognition using classifier ensembles
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
MOBIE: a Movie Brain Interactive Editor
SIGGRAPH Asia 2011 Emerging Technologies
Subliminally enhancing self-esteem: impact on learner performance and affective state
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
Brain interaction for mobile games
Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments
Understanding user experience in stereoscopic 3D games
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
BCS-HCI '11 Proceedings of the 25th BCS Conference on Human-Computer Interaction
From Mobie to Neu: 3D animated contents controlled by a brain-computer interface
Proceedings of the 2012 Virtual Reality International Conference
International Journal of Technology Enhanced Learning
| Hi-index | 0.00 |
This paper presents the results of a usability evaluation of the NeuroSky's MindSet (MS). Until recently most Brain Computer Interfaces (BCI) have been designed for clinical and research purposes partly due to their size and complexity. However, a new generation of consumer-oriented BCI has appeared for the video game industry. The MS, a headset with a single electrode, is based on electro-encephalogram readings (EEG) capturing faint electrical signals generated by neural activity. The electrical signal across the electrode is measured to determine levels of attention (based on Alpha waveforms) and then translated into binary data. This paper presents the results of an evaluation to assess the usability of the MS by defining a model of attention to fuse attention signals with user-generated data in a Second Life assessment exercise. The results of this evaluation suggest that the MS provides accurate readings regarding attention, since there is a positive correlation between measured and self-reported attention levels. The results also suggest there are some usability and technical problems with its operation. Future research is presented consisting of the definition a standardized reading methodology and an algorithm to level out the natural fluctuation of users' attention levels if they are to be used as inputs.