A Novel Computationally Efficient Approach for Exploring Neural Entrainment to Continuous Speech Stimuli Incorporating Cross-Correlation

A linear system identification technique has been widely used to track neural entrainment in response to continuous speech stimuli. Although the approach of the standard regularization method using ridge regression provides a straightforward solution to estimate and interpret neural responses to continuous speech stimuli, inconsistent results and costly computational processes can arise due to the need for parameter tuning. We developed a novel approach to the system identification method called the detrended cross-correlation function, which aims to map stimulus features to neural responses using the reverse correlation and derivative of convolution. This non-parametric (i.e., no need for parametric tuning) approach can maintain consistent results. Moreover, it provides a computationally efficient training process compared to the conventional method of ridge regression. The detrended cross-correlation function correctly captures the temporal response function to speech envelope and the spectral–temporal receptive field to speech spectrogram in univariate and multivariate forward models, respectively. The suggested model also provides more efficient computation compared to the ridge regression to process electroencephalography (EEG) signals. In conclusion, we suggest that the detrended cross-correlation function can be comparably used to investigate continuous speech- (or sound-) evoked EEG signals.

[1]  Aaron R. Nidiffer,et al.  Linear Modeling of Neurophysiological Responses to Speech and Other Continuous Stimuli: Methodological Considerations for Applied Research , 2021, Frontiers in Neuroscience.

[2]  M. Poo,et al.  Assessing the depth of language processing in patients with disorders of consciousness , 2020, Nature Neuroscience.

[3]  P. Vannasing,et al.  Functional Brain Connectivity of Language Functions in Children Revealed by EEG and MEG: A Systematic Review , 2020, Frontiers in Human Neuroscience.

[4]  Joshua P. Kulasingham,et al.  High gamma cortical processing of continuous speech in younger and older listeners , 2019, NeuroImage.

[5]  William Schuler,et al.  fMRI reveals language-specific predictive coding during naturalistic sentence comprehension , 2019, Neuropsychologia.

[6]  Napoleon Katsos,et al.  Bilingualism and language similarity modify the neural mechanisms of selective attention , 2019, Scientific Reports.

[7]  Edmund C. Lalor,et al.  Electrophysiological Correlates of Semantic Dissimilarity Reflect the Comprehension of Natural, Narrative Speech , 2017, Current Biology.

[8]  Christian Brodbeck,et al.  Neural source dynamics of brain responses to continuous stimuli: Speech processing from acoustics to comprehension , 2017, NeuroImage.

[9]  Edmund C. Lalor,et al.  The Multivariate Temporal Response Function (mTRF) Toolbox: A MATLAB Toolbox for Relating Neural Signals to Continuous Stimuli , 2016, Front. Hum. Neurosci..

[10]  John C. Mosher,et al.  Time-Frequency Strategies for Increasing High-Frequency Oscillation Detectability in Intracerebral EEG , 2016, IEEE Transactions on Biomedical Engineering.

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

[12]  Ying-Yee Kong,et al.  Differential modulation of auditory responses to attended and unattended speech in different listening conditions , 2014, Hearing Research.

[13]  Brian N. Pasley,et al.  Decoding spectrotemporal features of overt and covert speech from the human cortex , 2014, Front. Neuroeng..

[14]  Jonathan Z. Simon,et al.  Robust cortical entrainment to the speech envelope relies on the spectro-temporal fine structure , 2014, NeuroImage.

[15]  John J. Foxe,et al.  At what time is the cocktail party? A late locus of selective attention to natural speech , 2012, The European journal of neuroscience.

[16]  Michael J. Fogarty,et al.  Broad-scale climate influences on cod (Gadus morhua) recruitment on Georges Bank , 2011 .

[17]  John J. Foxe,et al.  Resolving precise temporal processing properties of the auditory system using continuous stimuli. , 2009, Journal of neurophysiology.

[18]  K. Tremblay,et al.  Speech Evoked Potentials: From the Laboratory to the Clinic , 2008, Ear and hearing.

[19]  D. Abrams,et al.  Right-Hemisphere Auditory Cortex Is Dominant for Coding Syllable Patterns in Speech , 2008, The Journal of Neuroscience.

[20]  T. Picton,et al.  Human Cortical Responses to the Speech Envelope , 2008, Ear and hearing.

[21]  Barak A. Pearlmutter,et al.  The VESPA: A method for the rapid estimation of a visual evoked potential , 2006, NeuroImage.

[22]  H. Newton,et al.  Neurology of the Arts: Painting, Music, Literature , 2006, Neurology.

[23]  Vasilis Z. Marmarelis,et al.  Nonlinear Dynamic Modeling of Physiological Systems: Marmarelis/Nonlinear , 2004 .

[24]  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.

[25]  Dario L. Ringach,et al.  Reverse correlation in neurophysiology , 2004, Cogn. Sci..

[26]  Christian K. Machens,et al.  Linearity of Cortical Receptive Fields Measured with Natural Sounds , 2004, The Journal of Neuroscience.

[27]  E Ahissar,et al.  Speech comprehension is correlated with temporal response patterns recorded from auditory cortex , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[28]  N. C. Singh,et al.  Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli , 2001 .

[29]  K. Sen,et al.  Spectral-temporal Receptive Fields of Nonlinear Auditory Neurons Obtained Using Natural Sounds , 2022 .

[30]  Michael W. Browne,et al.  Cross-Validation Methods. , 2000, Journal of mathematical psychology.

[31]  D. Heeger,et al.  Linear Systems Analysis of Functional Magnetic Resonance Imaging in Human V1 , 1996, The Journal of Neuroscience.

[32]  D. D. Greenwood A cochlear frequency-position function for several species--29 years later. , 1990, The Journal of the Acoustical Society of America.

[33]  P Kuyper,et al.  Triggered correlation. , 1968, IEEE transactions on bio-medical engineering.

[34]  이정학,et al.  문장인지검사를 위한 한국표준 문장표 개발 , 2008 .

[35]  Dietrich Lehmann,et al.  Human Evoked Potentials , 1979 .

[36]  Vasilis Z. Marmarelis,et al.  Analysis of Physiological Systems , 1978, Computers in Biology and Medicine.

[37]  Robert J. Polge,et al.  Impulse Response Determination by Cross Correlation , 1970, IEEE Transactions on Aerospace and Electronic Systems.