Sciweavers

Share
CHI
2008
ACM

Feasibility and pragmatics of classifying working memory load with an electroencephalograph

12 years 1 months ago
Feasibility and pragmatics of classifying working memory load with an electroencephalograph
A reliable and unobtrusive measurement of working memory load could be used to evaluate the efficacy of interfaces and to provide real-time user-state information to adaptive systems. In this paper, we describe an experiment we conducted to explore some of the issues around using an electroencephalograph (EEG) for classifying working memory load. Within this experiment, we present our classification methodology, including a novel feature selection scheme that seems to alleviate the need for complex drift modeling and artifact rejection. We demonstrate classification accuracies of up to 99% for 2 memory load levels and up to 88% for 4 levels. We also present results suggesting that we can do this with shorter windows, much less training data, and a smaller number of EEG channels, than reported previously. Finally, we show results suggesting that the models we construct transfer across variants of the task, implying some level of generality. We believe these findings extend prior work a...
David B. Grimes, Desney S. Tan, Scott E. Hudson, P
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2008
Where CHI
Authors David B. Grimes, Desney S. Tan, Scott E. Hudson, Pradeep Shenoy, Rajesh P. N. Rao
Comments (0)
books