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Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Editor: Franco Gori
  • Vol. 27, Iss. 12 — Dec. 1, 2010
  • pp: 2670–2683

Ideal observer analysis for task normalization of pattern classifier performance applied to EEG and fMRI data

Matthew F. Peterson, Koel Das, Jocelyn L. Sy, Sheng Li, Barry Giesbrecht, Zoe Kourtzi, and Miguel P. Eckstein  »View Author Affiliations


JOSA A, Vol. 27, Issue 12, pp. 2670-2683 (2010)
http://dx.doi.org/10.1364/JOSAA.27.002670


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Abstract

The application of multivariate techniques to neuroimaging and electrophysiological data has greatly enhanced the ability to detect where, when, and how functional neural information is processed during a variety of behavioral tasks. With the extension to single-trial analysis, neuroscientists are able to relate brain states to perceptual, cognitive, and motor processes. Using pattern classification methods, the neuroscientist can extract neural performance measures in a manner analogous to human behavioral performance, allowing for a consistent information content metric across measurement modalities. However, as with behavioral psychophysical performance, pattern classifier performances are a product of both the task-relevant information inherent in the brain and in the task/stimuli. Here, we argue for the use of an ideal observer framework with which the researcher can effectively normalize the observed neural performance given the task’s inherent objective difficulty. We use data from a face versus car discrimination task and compare classifier performance applied to electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data with corresponding human behavior through the absolute and relative efficiency metrics. We show that confounding variables that can lead to erroneous interpretations of information content can be accounted for through comparisons to an ideal observer, allowing for more confident interpretation of the neural mechanisms involved in the task of interest. Finally, we discuss limitations of interpretation due to the transduction of indirect measures of neural activity, underlying assumptions in the optimality of the pattern classifiers, and dependence of efficiency results on signal contrast.

© 2010 Optical Society of America

OCIS Codes
(330.4060) Vision, color, and visual optics : Vision modeling
(330.4300) Vision, color, and visual optics : Vision system - noninvasive assessment
(330.5510) Vision, color, and visual optics : Psychophysics

ToC Category:
Vision, Color, and Visual Optics

History
Original Manuscript: April 20, 2010
Revised Manuscript: October 8, 2010
Manuscript Accepted: October 19, 2010
Published: November 24, 2010

Citation
Matthew F. Peterson, Koel Das, Jocelyn L. Sy, Sheng Li, Barry Giesbrecht, Zoe Kourtzi, and Miguel P. Eckstein, "Ideal observer analysis for task normalization of pattern classifier performance applied to EEG and fMRI data," J. Opt. Soc. Am. A 27, 2670-2683 (2010)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-27-12-2670


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References

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