Optimal set of EEG electrodes for rapid serial visual presentation

Kenneth E. Hild, Santosh Mathan, Yonghong Huang, Deniz Erdogmus, Misha Pavel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

In our application, the goal is to search through a large image to find all instances of a pre-specified, high-valued target. One approach taken to increase the throughput of this image search task is to: chop the large image into numerous small images, display them to a user at high rates one-at-atime, and then search the simultaneously-recorded EEG data for neural activity that signifies that the user detected an instance of the target. The temporal efficiency of this EEGbased system is reduced by the overhead, which increases as the number of electrodes increases. Hence, we wish to find a minimal set of electrodes that ideally maintains the detection performance. In order to inform the design of future EEGbased image search systems, in this paper we find the 12 out of 32/64 most important electrodes for detection using 5 different feature selection methods. The optimal set includes all 5 occipital and the 2 most frontal electrodes.

Original languageEnglish (US)
Title of host publication2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
Pages4335-4338
Number of pages4
DOIs
StatePublished - 2010
Externally publishedYes
Event2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 - Buenos Aires, Argentina
Duration: Aug 31 2010Sep 4 2010

Publication series

Name2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10

Other

Other2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
Country/TerritoryArgentina
CityBuenos Aires
Period8/31/109/4/10

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Health Informatics

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