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Related Experiment Video

Updated: Mar 25, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

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Published on: October 24, 2012

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

Chun-Hsien Hsu¹, Chia-Ying Lee¹, Wei-Kuang Liang²

¹Institute of Linguistics, Academia Sinica, No. 128, Section 2, Academia Road, 115 Taipei, Taiwan, ROC.

Journal of Neuroscience Methods

|February 28, 2016

Summary

This summary is machine-generated.

Ensemble empirical mode decomposition (EEMD) offers a novel method for measuring mismatch negativity (MMN) with fewer trials and participants. This approach enhances signal-to-noise ratio, potentially aiding clinical applications.

Keywords:

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

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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

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Published on: June 27, 2013

Area of Science:

Neuroscience
Signal Processing

Background:

Mismatch negativity (MMN) is an event-related potential (ERP) typically requiring extensive trials and low-frequency band analysis.
Conventional MMN measurement involves averaging numerous trials (e.g., 1000 per participant) within a 2Hz-8Hz frequency band.

Purpose of the Study:

To demonstrate a new protocol using Ensemble Empirical Mode Decomposition (EEMD) and Hilbert spectral analyses to extract MMN-related activity.
To evaluate the efficacy of EEMD-derived event-related modes (ERMs) for MMN measurement.

Main Methods:

Applied EEMD to decompose electroencephalography (EEG) time series data into intrinsic mode functions (IMFs).
Averaged IMFs across trials to obtain ERMs for MMN analysis.
Investigated MMN responses to manipulated lexical tone changes in 18 participants.

Main Results:

ERMs revealed a significant effect of lexical tone on MMN amplitudes.
Significant MMN effects were detectable with approximately 300 trials per participant and small sample sizes (5-6 participants).

Conclusions:

EEMD method yields ERMs with high signal-to-noise ratios and strong effect sizes.
The EEMD approach reduces experimental requirements for MMN recording (fewer trials, smaller sample size).
ERMs show promise for MMN paradigm application in pediatric and clinical populations.