English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Characterizing neural entrainment to hierarchical linguistic units using electroencephalography (EEG)

MPS-Authors
/persons/resource/persons187736

Melloni,  Lucia
Department of Neuroscience, Max Planck Institute for Empirical Aesthetics, Max Planck Society;
Department of Neurology, New York University Langone Medical Center;
Department of Neurophysiology, Max-Planck Institute for Brain Research;

/persons/resource/persons173724

Poeppel,  David
Department of Neuroscience, Max Planck Institute for Empirical Aesthetics, Max Planck Society;
Department of Psychology, New York University;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

Melloni, Poeppel EEG.pdf
(Publisher version), 2MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Ding, N., Melloni, L., Yang, A., Wang, Y., Zhang, W., & Poeppel, D. (2017). Characterizing neural entrainment to hierarchical linguistic units using electroencephalography (EEG). Frontiers in Human Neuroscience, 11: 481. doi:10.3389/fnhum.2017.00481.


Cite as: https://hdl.handle.net/11858/00-001M-0000-002E-1073-F
Abstract
To understand speech, listeners have to combine the words they hear into phrases and sentences. Recent magnetoencephalography (MEG) and electrocorticography (ECoG)studies show that cortical activity is concurrently entrained/synchronized to the rhythms of multiple levels of linguistic units including words, phrases, and sentences. Here we investigate whether this phenomenon can be observed using electroencephalography (EEG), a technique that is more widely available than MEG and ECoG. We show that the EEG responses concurrently track the rhythms of hierarchical linguistic units such as syllables/words, phrases, and sentences. The strength of the sentential-rate response correlates with how well each subject can detect random words embedded in a sequence of sentences. In contrast, only a syllabic-rate response is observed for an unintelligible control stimulus. In sum, EEG provides a useful tool to characterize neural encoding of hierarchical linguistic units, potentially even in individual participants.