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Fully optimized discrimination of physiological responses to auditory stimuli

Stepan Y Kruglikov et al 2008 J. Neural Eng. 5 133-143   doi: 10.1088/1741-2560/5/2/004  Help

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Stepan Y Kruglikov1,2, Sharmila Chari1, Paul E Rapp3, Steven L Weinstein1,4, Barbara K Given1,5 and Steven J Schiff1,6,7
1 Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA 22030, USA
2 School of Computational Sciences, George Mason University, Fairfax, VA 22030, USA
3 Department of Pharmacology and Physiology, Drexel University, Philadelphia, PA 19129, USA
4 Department of Neurology, Children's National Medical Center, 111 Michigan Avenue, NW, Washington, DC 20010, USA
5 Graduate School of Education, George Mason University, Fairfax, VA 22030, USA
6 Department of Psychology and Program in Neuroscience, George Mason University, Fairfax, VA 22030, USA
7 Center for Neural Engineering, Departments of Neurosurgery, Engineering Science and Mechanics, and Physics, The Pennsylvania State University, University Park, PA 16802, USA
E-mail: sschiff@psu.edu

Abstract. The use of multivariate measurements to characterize brain activity (electrical, magnetic, optical) is widespread. The most common approaches to reduce the complexity of such observations include principal and independent component analyses (PCA and ICA), which are not well suited for discrimination tasks. We addressed two questions: first, how do the neurophysiological responses to elongated phonemes relate to tone and phoneme responses in normal children, and, second, how discriminable are these responses. We employed fully optimized linear discrimination analysis to maximally separate the multi-electrode responses to tones and phonemes, and classified the response to elongated phonemes. We find that discrimination between tones and phonemes is dependent upon responses from associative regions of the brain apparently distinct from the primary sensory cortices typically emphasized by PCA or ICA, and that the neuronal correlates corresponding to elongated phonemes are highly variable in normal children (about half respond with neural correlates of tones and half as phonemes). Our approach is made feasible by the increase in computational power of ordinary personal computers and has significant advantages for a wide range of neuronal imaging modalities.

Print publication: Issue 2 (June 2008)
Received 19 November 2007, accepted for publication 19 March 2008
Published 22 April 2008

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