Abstract
IntroductionScalp‐recorded electrophysiological responses to complex, periodic auditory signals reflect phase‐locked activity from neural ensembles within the auditory system. These responses, referred to as frequency‐following responses (FFRs), have been widely utilized to index typical and atypical representation of speech signals in the auditory system. One of the major limitations in FFR is the low signal‐to‐noise ratio at the level of single trials. For this reason, the analysis relies on averaging across thousands of trials. The ability to examine the quality of single‐trial FFRs will allow investigation of trial‐by‐trial dynamics of the FFR, which has been impossible due to the averaging approach.MethodsIn a novel, data‐driven approach, we used machine learning principles to decode information related to the speech signal from single trial FFRs. FFRs were collected from participants while they listened to two vowels produced by two speakers. Scalp‐recorded electrophysiological responses were projected onto a low‐dimensional spectral feature space independently derived from the same two vowels produced by 40 speakers, which were not presented to the participants. A novel supervised machine learning classifier was trained to discriminate vowel tokens on a subset of FFRs from each participant, and tested on the remaining subset.ResultsWe demonstrate reliable decoding of speech signals at the level of single‐trials by decomposing the raw FFR based on information‐bearing spectral features in the speech signal that were independently derived.ConclusionsTaken together, the ability to extract interpretable features at the level of single‐trials in a data‐driven manner offers unchartered possibilities in the noninvasive assessment of human auditory function.
Highlights
Scalp-recorded electrophysiological responses to complex, periodic auditory signals reflect phase-locked activity from neural ensembles within the auditory system
The stimuli were [æ] and [u] vowels produced by two male native English speakers (Figure 1a), which were not used in the construction of the spectral feature space (Hillenbrand et al, 1995), from which the vowel nuclei were extracted using the documented start and end time points, and duration normalized to 250 ms and RMS amplitude normalized to 70 dB sound pressure level
We demonstrate an innovative application of machine learning principles to reliably extract vowel information from the single-t rial speech- evoked frequency-following responses (FFRs)
Summary
Scalp-recorded electrophysiological responses to complex auditory signals closely resemble the acoustic properties of the stimuli. Direct electrocorticography methods have been used to show that the firing patterns of cortical neurons can be used to reliably discriminate English phonemes (Mesgarani et al, 2014; Pei et al, 2011), which has been replicated with a noninvasive approach of recording cortical activity (Hausfeld et al, 2012) Unlike their cortical counterparts, the FFR closely mimics the spectrotemporal properties of the original auditory stimuli (Bidelman, 2014), to the degree that listeners can recognize words from the neural responses that have been converted into sound stimuli (Galbraith, Arbagey, Branski, Comerci, & Rector, 1995). We used a novel machine learning approach to decode vowels from single trial, speech-evoked FFRs. We focused on the spectral features observable in the FFR to the vowel sounds, and examine the extent to which vowel related features could be used to classify the stimuli. We demonstrate that phonological information can be extracted from single-trial FFR using a machine learning approach based on interpretable spectral features
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
