Hsu Chun-Hsien, Lee Chia-Ying, Liang Wei-Kuang
Institute of Linguistics, Academia Sinica, No. 128, Section 2, Academia Road, 115 Taipei, Taiwan, ROC.
Institute of Linguistics, Academia Sinica, No. 128, Section 2, Academia Road, 115 Taipei, Taiwan, ROC.
J Neurosci Methods. 2016 May 1;264:78-85. doi: 10.1016/j.jneumeth.2016.02.015. Epub 2016 Feb 23.
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.
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.
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.
ERMs may be useful for applying the MMN paradigm in clinical populations and children.
失配负波（MMN）是事件相关电位（ERP）的一个组成部分。测量MMN的传统方法包括记录大量试验（例如，每位参与者1000次试验）并在低频带（例如，2赫兹至8赫兹之间）提取信号。
总体经验模态分解（EEMD）是一种将时间序列数据分解为固有模态函数（IMF）的方法。每个IMF都有一个主导频率。与ERP测量类似，对各试验的IMF进行平均可测量事件相关模态（ERM）。本文展示了一种方案，该方案采用EEMD和希尔伯特谱分析，并基于在MMN范式中从18名参与者记录的脑电图数据，使用ERM提取与MMN相关的活动。通过操纵词汇声调的变化来证明偏差的影响。
ERM的平均幅度显示词汇声调对MMN有显著影响。基于效应量统计，在小样本量（五至六名参与者）中，使用ERM测量，在较少试验次数（每位参与者约300次试验）的情况下，可观察到词汇声调对MMN有显著影响。
EEMD方法为ERM提供了非常高的信噪比，并产生了很强的效应量。此外，在使用建议的分析方法时，记录MMN的实验要求（即试验次数和样本量）可以降低。
ERM可能有助于将MMN范式应用于临床人群和儿童。
