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

- We adopt EEMD method and evaluate the statistical significance of ERM measurements.
- ERMs can reflect effects of lexical tone changes even with a less number of trials.
- EEMD method is helpful to promote clinical and developmental assessments based on EEG.

BackgroundMismatch 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 MethodEnsemble 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.ResultsThe 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.ConclusionsERMs may be useful for applying the MMN paradigm in clinical populations and children.