Chun-Hsien Hsu1, Chia-Ying Lee2, Wei-Kuang Liang3. 1. Institute of Linguistics, Academia Sinica, No. 128, Section 2, Academia Road, 115 Taipei, Taiwan, ROC. Electronic address: kevinhsu@gate.sinica.edu.tw. 2. Institute of Linguistics, Academia Sinica, No. 128, Section 2, Academia Road, 115 Taipei, Taiwan, ROC. 3. Institute of Cognitive Neuroscience, National Central University, Jhongli, Taiwan, ROC.
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.
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.
Authors: Carlos Amo; Luis de Santiago; Rafael Barea; Almudena López-Dorado; Luciano Boquete Journal: Sensors (Basel) Date: 2017-04-29 Impact factor: 3.576
Authors: Trung Van Nguyen; Che-Yi Hsu; Satish Jaiswal; Neil G Muggleton; Wei-Kuang Liang; Chi-Hung Juan Journal: Front Hum Neurosci Date: 2021-01-28 Impact factor: 3.169
Authors: Shao-Yang Tsai; Satish Jaiswal; Chi-Fu Chang; Wei-Kuang Liang; Neil G Muggleton; Chi-Hung Juan Journal: Front Integr Neurosci Date: 2018-05-15