About Contact Help us

HCI, User Experience, Information Architecture, Usability, and more

“It is easy for me to access this knowledge pool, I want it to grow so that I can grow along”

---Maria Hull

Last 3 Donors

1. Stephen J. Kidd

2. Maria Hull

3. Rosemary J. Bennett

...more generous donors

Support us

Funding progress for 2010:

References

Authors

Pradeep Shenoy

Printer-friendly

How to cite

Pradeep Shenoy

About the author:
No description available of Pradeep Shenoy...

ADD DESCRIPTION

← Button will not work: You have not enabled Javascript. How do I enable it?

ADD PUBLICATION

← Button will not work: You have not enabled Javascript. How do I enable it?

SHARE YOUR RESEARCH

← Button will not work: You have not enabled Javascript. How do I enable it?

Publications by Pradeep Shenoy (bibliography)

» 2008 «

Edit | Del

Grimes, David, Tan, Desney S., Hudson, Scott E., Shenoy, Pradeep and Rao, Rajesh P. N. (2008): Feasibility and pragmatics of classifying working memory load with an electroencephalograph. In: Proceedings of ACM CHI 2008 Conference on Human Factors in Computing Systems April 5-10, 2008. pp. 835-844. Available online

“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 and bring us a step closer to the use of such technologies in HCI research.”

Copyrights may apply

Edit | Del

Shenoy, Pradeep and Tan, Desney S. (2008): Human-aided computing: utilizing implicit human processing to classify images. In: Proceedings of ACM CHI 2008 Conference on Human Factors in Computing Systems April 5-10, 2008. pp. 845-854. Available online

“In this paper, we present Human-Aided Computing, an approach that uses an electroencephalograph (EEG) device to measure the presence and outcomes of implicit cognitive processing, processing that users perform automatically and may not even be aware of. We describe a classification system and present results from two experiments as proof-of-concept. Results from the first experiment showed that our system could classify whether a user was looking at an image of a face or not, even when the user was not explicitly trying to make this determination. Results from the second experiment extended this to animals and inanimate object categories as well, suggesting generality beyond face recognition. We further show that we can improve classification accuracies if we show images multiple times, potentially to multiple people, attaining well above 90% classification accuracies with even just ten presentations.”

Copyrights may apply

ADD PUBLICATION