An improved method for measuring mismatch negativity using ensemble empirical mode decomposition

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Abstract

Background: Mismatch negativity (MMN) is a component of event-related potentials (ERPs). Conventional approaches to measuring MMN include recording a large number of trials (e.g., 1000 trials per participant) and extracting signals within a low frequency band, e.g., between 2 Hz and 8 Hz. New Method: Ensemble empirical mode decomposition (EEMD) is a method to decompose time series data into intrinsic mode functions (IMFs). Each IMF has a dominant frequency. Similar to ERP measurement, averaging IMFs across trials allows measurement of event-related modes (ERMs). This paper demonstrates a protocol that adopts EEMD and Hilbert spectral analyses and uses ERMs to extract MMN-related activity based on electroencephalography data recorded from 18 participants in an MMN paradigm. The effect of deviants was demonstrated by manipulating changes in lexical tones. Results: The mean amplitudes of ERMs revealed a significant effect of lexical tone on MMN. Based on effect size statistics, a significant effect of lexical tone on MMN could be observed using ERM measurements over fewer trials (about 300 trials per participant) in a small sample size (five to six participants). Comparison with Existing Method(s): The EEMD method provided ERMs with remarkably high signal-to-noise ratios and yielded a strong effect size. Furthermore, the experimental requirements for recording MMN (i.e., the number of trials and the sample size) could be reduced while using the suggested analytic method. Conclusions: ERMs may be useful for applying the MMN paradigm in clinical populations and children.

Original languageEnglish
Pages (from-to)78-85
Number of pages8
JournalJournal of Neuroscience Methods
Volume264
DOIs
StatePublished - 1 May 2016

Keywords

  • Ensemble empirical mode decomposition
  • Event-related mode
  • Event-related potential
  • Hilbert-huang transformation
  • Intrinsic mode function
  • Mismatch negativity

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