Better brain interfacing for the masses: progress in event-related potential detection using commercial brain computer interfaces

Grierson, Mick and Kiefer, Chris (2011) Better brain interfacing for the masses: progress in event-related potential detection using commercial brain computer interfaces. In: CHI '11 Extended Abstracts on Human Factors in Computing Systems, May 7 - 12, 2011, Vancouver, Canada.

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Abstract

Event-Related Potential (ERP) techniques are commonly used by researchers from a range of disciplines including psychology and medicine to stimulate meaningful ERP signals from the brain and interpret them through Electroencephalography (EEG). ERP signals are in most cases able to reliably reflect cognitive processes, and are widely used in Brain Computer Interface (BCI) research. We present work in progress towards the application of these techniques to emerging consumer-grade BCI technology. Our approach has an impact on the reliability and usability of consumer Brain Computer Interfaces in commercial contexts, and is already being adopted by our industry partners in the games and entertainment sector. It could also significantly reduce the cost and complexity of certain types of large scale ERP research. This work is being undertaken by the Embodied AudioVisual Interaction (EAVI) group at Goldsmiths, University of London, and is supported by the Arts and Humanities Research Council.

Item Type: Conference or Workshop Item (Poster)
Keywords: brain computer interface, computer-mediated communication, emotion and affective user interface, event related potentials, neurosky, usability research
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: M Music. Literature on music. Musical instruction and study > M Music > M0005 Instrumental music > M1470 Aleatory music. Electronic music. Mixed media
Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
Q Science > QA Mathematics > QA0076 Computer software
T Technology > T Technology (General) > T0055.4 Industrial engineering. Management engineering > T0059.7 Human engineering in industry. Man-machine systems
Depositing User: Chris Kiefer
Date Deposited: 05 Jan 2015 08:22
Last Modified: 05 Jan 2015 08:22
URI: http://sro.sussex.ac.uk/id/eprint/51866

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