Remarks on the analysis of steady-state responses: Spurious artifacts introduced by overlapping epochs

被引:19
作者
Benjamin, Lucas [1 ]
Dehaene-Lambertz, Ghislaine [1 ]
Flo, Ana [1 ]
机构
[1] Univ Paris Saclay, NeuroSpin Ctr, Cognit Neuroimaging Unit, CNRS ERL 9003,INSERM U992,CEA, Gif Sur Yvette, France
基金
欧洲研究理事会;
关键词
OSCILLATIONS;
D O I
10.1016/j.cortex.2021.05.023
中图分类号
B84 [心理学]; C [社会科学总论]; Q98 [人类学];
学科分类号
03 ; 0303 ; 030303 ; 04 ; 0402 ;
摘要
Periodic and stable sensory input can result in rhythmic and stable neural responses, a phenomenon commonly referred to as neural entrainment. Although the use of neural entrainment to investigate the regularities the brain tracks has increased in recent years, the methods used for its quantification are not well-defined in the literature. Here we argue that some strategies used in previous papers, are inadequate for the study of steady-state response, and lead to methodological artefacts. The aim of this commentary is to discuss these articles and to propose alternative measures of neural entrainment. Specifically, we applied four possible alternatives and two epoching approaches reported in the literature to quantify neural entrainment on simulated datasets. Our results demonstrate that overlapping epochs, as used in the original Batterink and colleagues articles, inevitably lead to a methodological artefact at the frequency corresponding to the overlap. We therefore strongly discourage this approach and encourage the re-analysis of data based on overlapping epochs. Additionally, we argue that the use of time-frequency decomposition to compute phase coherence at low frequencies to reveal neural entrainment is not optimal. (C) 2021 Elsevier Ltd. All rights reserved.
引用
收藏
页码:370 / 378
页数:9
相关论文
共 20 条
[1]   Statistical learning of speech regularities can occur outside the focus of attention [J].
Batterink, Laura J. ;
Paller, Ken A. .
CORTEX, 2019, 115 :56-71
[2]   Online neural monitoring of statistical learning [J].
Batterink, Laura J. ;
Paller, Ken A. .
CORTEX, 2017, 90 :31-45
[3]   Investigating the neural correlates of continuous speech computation with frequency-tagged neuroelectric responses [J].
Buiatti, Marco ;
Pena, Marcela ;
Dehaene-Lambertz, Ghislaine .
NEUROIMAGE, 2009, 44 (02) :509-519
[4]  
Buzsaki G., 2006, Rhythms of the Brain, DOI DOI 10.1093/ACPROF:OSO/9780195301069.001.0001
[5]   Steady-State Visual Evoked Potentials Can Be Explained by Temporal Superposition of Transient Event-Related Responses [J].
Capilla, Almudena ;
Pazo-Alvarez, Paula ;
Darriba, Alvaro ;
Campo, Pablo ;
Gross, Joachim .
PLOS ONE, 2011, 6 (01)
[6]   Preverbal Infants Discover Statistical Word Patterns at Similar Rates as Adults: Evidence From Neural Entrainment [J].
Choi, Dawoon ;
Batterink, Laura J. ;
Black, Alexis K. ;
Paller, Ken A. ;
Werker, Janet F. .
PSYCHOLOGICAL SCIENCE, 2020, 31 (09) :1161-1173
[7]   Rapid categorization of natural face images in the infant right hemisphere [J].
de Heering, Adelaide ;
Rossion, Bruno .
ELIFE, 2015, 4 :1-14
[8]   EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis [J].
Delorme, A ;
Makeig, S .
JOURNAL OF NEUROSCIENCE METHODS, 2004, 134 (01) :9-21
[9]   Characterizing Neural Entrainment to Hierarchical Linguistic Units using Electroencephalography (EEG) [J].
Ding, Nai ;
Melloni, Lucia ;
Yang, Aotian ;
Wang, Yu ;
Zhang, Wen ;
Poeppel, David .
FRONTIERS IN HUMAN NEUROSCIENCE, 2017, 11
[10]   Cortical tracking of hierarchical linguistic structures in connected speech [J].
Ding, Nai ;
Melloni, Lucia ;
Zhang, Hang ;
Tian, Xing ;
Poeppel, David .
NATURE NEUROSCIENCE, 2016, 19 (01) :158-+