The effect of stimulus intensity on neural envelope tracking

Objectives The last years there has been significant interest in attempting to recover the temporal envelope of a speech signal from the neural response to investigate neural speech processing. The research focus is now broadening from neural speech processing in normal-hearing listeners towards hearing-impaired listeners. When testing hearing-impaired listeners speech has to be amplified to resemble the effect of a hearing aid and compensate peripheral hearing loss. Until today, it is not known with certainty how or if neural speech tracking is influenced by sound amplification. As these higher intensities could influence the outcome, we investigated the influence of stimulus intensity on neural speech tracking. Design We recorded the electroencephalogram (EEG) of 20 normal-hearing participants while they listened to a narrated story. The story was presented at intensities from 10 to 80 dB A. To investigate the brain responses, we analyzed neural tracking of the speech envelope by reconstructing the envelope from EEG using a linear decoder and by correlating the reconstructed with the actual envelope. We investigated the delta (0.5-4 Hz) and the theta (4-8 Hz) band for each intensity. We also investigated the latencies and amplitudes of the responses in more detail using temporal response functions which are the estimated linear response functions between the stimulus envelope and the EEG. Results Neural envelope tracking is dependent on stimulus intensity in both the TRF and envelope reconstruction analysis. However, provided that the decoder is applied on data of the same stimulus intensity as it was trained on, envelope reconstruction is robust to stimulus intensity. In addition, neural envelope tracking in the delta (but not theta) band seems to relate to speech intelligibility. Similar to the linear decoder analysis, TRF amplitudes and latencies are dependent on stimulus intensity: The amplitude of peak 1 (30-50 ms) increases and the latency of peak 2 (140-160 ms) decreases with increasing stimulus intensity. Conclusion Although brain responses are influenced by stimulus intensity, neural envelope tracking is robust to stimulus intensity when using the same intensity to test and train the decoder. Therefore we can assume that intensity is not a confound when testing hearing-impaired participants with amplified speech using the linear decoder approach. In addition, neural envelope tracking in the delta band appears to be correlated with speech intelligibility, showing the potential of neural envelope tracking as an objective measure of speech intelligibility.

[1]  Thomas Lunner,et al.  Neural tracking of attended versus ignored speech is differentially affected by hearing loss. , 2017, Journal of neurophysiology.

[2]  Jonathan Z. Simon,et al.  Adaptive Temporal Encoding Leads to a Background-Insensitive Cortical Representation of Speech , 2013, The Journal of Neuroscience.

[3]  T. Francart,et al.  The Effect of Stimulus Choice on an EEG-Based Objective Measure of Speech Intelligibility. , 2020, Ear and hearing.

[4]  E. J. Moore,et al.  Variability of Latency and Amplitude of Acoustically Evoked Responses to Pure Tones of Moderate to High Intensity , 1969 .

[5]  Lucas C Parra,et al.  EEG can predict speech intelligibility , 2019, Journal of neural engineering.

[6]  Birger Kollmeier,et al.  Effect of Speech Rate on Neural Tracking of Speech , 2019, Front. Psychol..

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

[8]  J. Simon,et al.  Neural coding of continuous speech in auditory cortex during monaural and dichotic listening. , 2012, Journal of neurophysiology.

[9]  Ben Somers,et al.  Neural envelope tracking as a measure of speech understanding in cochlear implant users , 2019, Hearing Research.

[10]  R V Shannon,et al.  Speech Recognition with Primarily Temporal Cues , 1995, Science.

[11]  G. A. Mack,et al.  On the Use of a Friedman-Type Statistic in Balanced and Unbalanced Block Designs , 1981 .

[12]  Tom Francart,et al.  Predicting individual speech intelligibility from the neural tracking of acoustic- and phonetic-level speech representations , 2018, bioRxiv.

[13]  Nicola Molinaro,et al.  Delta(but not theta)‐band cortical entrainment involves speech‐specific processing , 2018, The European journal of neuroscience.

[14]  Maaike Van Eeckhoutte,et al.  Auditory steady-state responses as neural correlates of loudness growth , 2016, Hearing Research.

[15]  W James,et al.  New Handbook Of Auditory Evoked Responses >>>CLICK HERE<<< , 2007 .

[16]  Stefan Haufe,et al.  On the interpretation of weight vectors of linear models in multivariate neuroimaging , 2014, NeuroImage.

[17]  John J. Foxe,et al.  Attentional Selection in a Cocktail Party Environment Can Be Decoded from Single-Trial EEG. , 2015, Cerebral cortex.

[18]  J. Simon,et al.  Emergence of neural encoding of auditory objects while listening to competing speakers , 2012, Proceedings of the National Academy of Sciences.

[19]  Edmund C. Lalor,et al.  Causal cortical dynamics of a predictive enhancement of speech intelligibility , 2018, NeuroImage.

[20]  Curtis J. Billings,et al.  Effects of Hearing Aid Amplification and Stimulus Intensity on Cortical Auditory Evoked Potentials , 2007, Audiology and Neurotology.

[21]  James W. Hall.  Handbook of Auditory Evoked Responses , 1991 .

[22]  W D Keidel,et al.  An investigation of the human cortical evoked potential under conditions of monaural and binaural stimulation. , 1969, Acta oto-laryngologica.

[23]  Alessandro Presacco,et al.  Speech-in-noise representation in the aging midbrain and cortex: Effects of hearing loss , 2019, PloS one.

[24]  J. Simon,et al.  Cortical entrainment to continuous speech: functional roles and interpretations , 2014, Front. Hum. Neurosci..

[25]  Tom Francart,et al.  Evidence for enhanced neural tracking of the speech envelope underlying age-related speech-in-noise difficulties. , 2019, Journal of neurophysiology.

[26]  Alexander Bertrand,et al.  The effect of head-related filtering and ear-specific decoding bias on auditory attention detection , 2016, Journal of neural engineering.

[27]  S. David,et al.  Estimating sparse spectro-temporal receptive fields with natural stimuli , 2007, Network.

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

[29]  Jan Wouters,et al.  Speech Intelligibility Predicted from Neural Entrainment of the Speech Envelope , 2018, bioRxiv.

[30]  Lars Meyer,et al.  Linguistic Bias Modulates Interpretation of Speech via Neural Delta-Band Oscillations , 2016, Cerebral cortex.

[31]  Tom Francart,et al.  Hearing impairment is associated with enhanced neural tracking of the speech envelope , 2019, Hearing Research.

[32]  Tom Francart,et al.  Evidence for enhanced neural tracking of the speech envelope underlying age-related speech-in-noise difficulties , 2018, bioRxiv.

[33]  G. Adler,et al.  Influence of stimulus intensity on AEP components in the 80- to 200-millisecond latency range. , 1989, Audiology : official organ of the International Society of Audiology.

[34]  Tom Francart,et al.  Effect of Task and Attention on Neural Tracking of Speech , 2019, bioRxiv.

[35]  Tobias Neher,et al.  Effects of directional sound processing and listener's motivation on EEG responses to continuous noisy speech: Do normal-hearing and aided hearing-impaired listeners differ? , 2019, Hearing Research.

[36]  Alexander Bertrand,et al.  Auditory-Inspired Speech Envelope Extraction Methods for Improved EEG-Based Auditory Attention Detection in a Cocktail Party Scenario , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[37]  D. Lesenfants,et al.  Predicting individual speech intelligibility from the cortical tracking of acoustic- and phonetic-level speech representations , 2019, Hearing Research.

[38]  Alexander Bertrand,et al.  A generic EEG artifact removal algorithm based on the multi-channel Wiener filter , 2018, Journal of neural engineering.

[39]  Mitsunori Mizumachi,et al.  WHAT IS SPEECH INTELLIGIBILITY? , 2017 .

[40]  T W Picton,et al.  Amplitude of evoked responses to tones of high intensity. , 1970, Acta oto-laryngologica.

[41]  Tom Francart,et al.  Top-down modulation of neural envelope tracking: the interplay with behavioral, self-report and neural measures of listening effort , 2019, bioRxiv.