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Scalp‐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.

In 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 …