Characterizing Neural Entrainment to Hierarchical Linguistic Units using Electroencephalography (EEG)

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.

[1]  R. Jackendoff Foundations of Language: Brain, Meaning, Grammar, Evolution , 2002 .

[2]  C. Phillips Linear Order and Constituency , 2003, Linguistic Inquiry.

[3]  D. Tucker,et al.  Scalp electrode impedance, infection risk, and EEG data quality , 2001, Clinical Neurophysiology.

[4]  Z. Harris,et al.  Foundations of language , 1941 .

[5]  Angela D. Friederici,et al.  Brain potentials indicate immediate use of prosodic cues in natural speech processing , 1999, Nature Neuroscience.

[6]  Peter Hagoort,et al.  Frequency-based Segregation of Syntactic and Semantic Unification during Online Sentence Level Language Comprehension , 2015, Journal of Cognitive Neuroscience.

[7]  Edmund C. Lalor,et al.  Low-Frequency Cortical Entrainment to Speech Reflects Phoneme-Level Processing , 2015, Current Biology.

[8]  Marco Buiatti,et al.  Investigating the neural correlates of continuous speech computation with frequency-tagged neuroelectric responses , 2009, NeuroImage.

[9]  Antoine J. Shahin,et al.  Attentional Gain Control of Ongoing Cortical Speech Representations in a “Cocktail Party” , 2010, The Journal of Neuroscience.

[10]  Kuansan Wang,et al.  Auditory representations of acoustic signals , 1992, IEEE Trans. Inf. Theory.

[11]  D. Poeppel,et al.  Phase Patterns of Neuronal Responses Reliably Discriminate Speech in Human Auditory Cortex , 2007, Neuron.

[12]  Ankoor S. Shah,et al.  An oscillatory hierarchy controlling neuronal excitability and stimulus processing in the auditory cortex. , 2005, Journal of neurophysiology.

[13]  Lucia Melloni,et al.  Brain Oscillations during Spoken Sentence Processing , 2012, Journal of Cognitive Neuroscience.

[14]  M. Berger,et al.  High Gamma Power Is Phase-Locked to Theta Oscillations in Human Neocortex , 2006, Science.

[15]  Stanislas Dehaene,et al.  Neurophysiological dynamics of phrase-structure building during sentence processing , 2017, Proceedings of the National Academy of Sciences.

[16]  W. Li,et al.  Perception of prosodic hierarchical boundaries in Mandarin Chinese sentences , 2009, Neuroscience.

[17]  Philippe Peigneux,et al.  Auditory Magnetoencephalographic Frequency-Tagged Responses Mirror the Ongoing Segmentation Processes Underlying Statistical Learning , 2016, Brain Topography.

[18]  Thomas G. Bever,et al.  Sentence Comprehension: The Integration of Habits and Rules , 2001 .

[19]  Noam Chomsky,et al.  Structures, Not Strings: Linguistics as Part of the Cognitive Sciences , 2015, Trends in Cognitive Sciences.

[20]  Angela D. Friederici,et al.  Artificial grammar learning meets formal language theory: an overview , 2012, Philosophical Transactions of the Royal Society B: Biological Sciences.

[21]  C. Honey,et al.  Topographic Mapping of a Hierarchy of Temporal Receptive Windows Using a Narrated Story , 2011, The Journal of Neuroscience.

[22]  John J. Foxe,et al.  Neural responses to uninterrupted natural speech can be extracted with precise temporal resolution , 2010, The European journal of neuroscience.

[23]  M. Buiatti,et al.  Electrophysiological evidence of statistical learning of long-distance dependencies in 8-month-old preterm and full-term infants , 2015, Brain and Language.

[24]  Noam Chomsky,et al.  Evolution, brain, and the nature of language , 2013, Trends in Cognitive Sciences.

[25]  Y. Nir,et al.  Sleep Disrupts High-Level Speech Parsing Despite Significant Basic Auditory Processing , 2017, The Journal of Neuroscience.

[26]  R. Diehl,et al.  Speech Perception , 2004, Annual review of psychology.

[27]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[28]  Morten H. Christiansen,et al.  How hierarchical is language use? , 2012, Proceedings of the Royal Society B: Biological Sciences.

[29]  W. Luh,et al.  Abstract linguistic structure correlates with temporal activity during naturalistic comprehension , 2016, Brain and Language.

[30]  A. Friederici,et al.  Auditory Language Comprehension: An Event-Related fMRI Study on the Processing of Syntactic and Lexical Information , 2000, Brain and Language.

[31]  Nick Chater,et al.  The Now-or-Never bottleneck: A fundamental constraint on language , 2015, Behavioral and Brain Sciences.

[32]  D. Poeppel,et al.  Cortical Tracking of Hierarchical Linguistic Structures in Connected Speech , 2015, Nature Neuroscience.

[33]  Elissa L. Newport,et al.  Segmenting nonsense: an event-related potential index of perceived onsets in continuous speech , 2002, Nature Neuroscience.

[34]  Ken A. Paller,et al.  Online neural monitoring of statistical learning , 2017, Cortex.

[35]  Peter Hagoort,et al.  Syntactic Unification Operations Are Reflected in Oscillatory Dynamics during On-line Sentence Comprehension , 2010, Journal of Cognitive Neuroscience.

[36]  D. Poeppel,et al.  Speech perception at the interface of neurobiology and linguistics , 2008, Philosophical Transactions of the Royal Society B: Biological Sciences.

[37]  S. Dehaene,et al.  Cortical representation of the constituent structure of sentences , 2011, Proceedings of the National Academy of Sciences.