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

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 2Hz and 8Hz.

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.

Keywords: Ensemble empirical mode decomposition; Event-related mode; Event-related potential; Hilbert–Huang transformation; Intrinsic mode function; Mismatch negativity.