Vowel decoding from single-trial speech-evoked electrophysiological responses: A feature-based machine learning approach.

Journal: Brain and behavior
PMID: 28638700

Abstract

INTRODUCTION: Scalp-recorded electrophysiological responses to complex, periodic auditory signals reflect phase-locked activity from neural ensembles within the auditory system. These responses, referred to as frequency-following responses (FFRs), have been widely utilized to index typical and atypical representation of speech signals in the auditory system. One of the major limitations in FFR is the low signal-to-noise ratio at the level of single trials. For this reason, the analysis relies on averaging across thousands of trials. The ability to examine the quality of single-trial FFRs will allow investigation of trial-by-trial dynamics of the FFR, which has been impossible due to the averaging approach.

Authors

  • Han G Yi
    Department of Communication Sciences & Disorders Moody College of Communication The University of Texas at Austin Austin TX USA.
  • Zilong Xie
    Department of Communication Sciences & Disorders Moody College of Communication The University of Texas at Austin Austin TX USA.
  • Rachel Reetzke
    Department of Communication Sciences & Disorders Moody College of Communication The University of Texas at Austin Austin TX USA.
  • Alexandros G Dimakis
    Department of Electrical and Computer Engineering Cockrell School of Engineering The University of Texas at Austin Austin TX USA.
  • Bharath Chandrasekaran
    Department of Communication Sciences & Disorders Moody College of Communication The University of Texas at Austin Austin TX USA.

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