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

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題名:	An improved method for measuring mismatch negativity using ensemble empirical mode decomposition
作者:	李佳穎;Hsu, Chun-Hsien;Lee, Chia-Ying;Liang, Wei-Kuang
貢獻者:	生醫理工學院認知神經科學研究所
關鍵詞:	Adolescent;Adult;Electroencephalography - instrumentation;Electroencephalography - methods;Ensemble empirical mode decomposition;Event-related mode;Event-related potential;Evoked Potentials - physiology;Evoked Potentials, Auditory - physiology;Female;Hilbert–Huang transformation;Humans;Intrinsic mode function;Male;Mismatch negativity;Signal Processing, Computer-Assisted;Young Adult
日期:	2016-05-01
上傳時間:	2026-04-23 14:18:49 (UTC+8)
出版者:	Elsevier;Netherlands: Elsevier B.V
摘要:	摘要： •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. 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). 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. 其他題名： J Neurosci Methods 出版者： Netherlands: Elsevier B.V 出版日期： 2016-05-01 出處： Journal of neuroscience methods, 2016-05, Vol.264, p.78-85 版權： 2016 Elsevier B.V. 版權： Copyright © 2016 Elsevier B.V. All rights reserved. 識別號： ISSN: 0165-0270 識別號： EISSN: 1872-678X 識別號： DOI: 10.1016/j.jneumeth.2016.02.015 識別號： PMID: 26920929
顯示於類別:	[認知與神經科學研究所 ] 期刊論文

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