research-article

Feasibility and pragmatics of classifying working memory load with an electroencephalograph

Authors:

Scott E. Hudson,

Pradeep Shenoy,

Rajesh P.N. RaoAuthors Info & Claims

CHI '08: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 835 - 844

https://doi.org/10.1145/1357054.1357187

Published: 06 April 2008 Publication History

Abstract

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 con-ducted to explore some of the issues around using an elec-troencephalograph (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 and bring us a step closer to the use of such technologies in HCI research.

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Feasibility and pragmatics of classifying working memory load with an electroencephalograph

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cover image ACM Conferences

CHI '08: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2008

1870 pages

ISBN:9781605580111

DOI:10.1145/1357054

General Chairs:
Mary Czerwinski
Microsoft Research, USA
,
Arnie Lund
Microsoft, USA
,
Program Chair:
Desney Tan
Microsoft Research, USA

Copyright © 2008 ACM.

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Published: 06 April 2008

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Niforatos EHe TVourvopoulos AGiannakos M(2024)Democratizing EEG: Embedding Electroencephalography in a Head-Mounted Display for Ubiquitous Brain-Computer InterfacingInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2388368(1-25)Online publication date: 19-Aug-2024
https://doi.org/10.1080/10447318.2024.2388368
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https://doi.org/10.1007/978-3-031-58396-4_2
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