Enhanced Neural Tracking of the Fundamental Frequency of the Voice.

Abstract

'F0 tracking' is a novel method that investigates neural processing of the fundamental frequency of the voice (f0) in continuous speech. Using linear modelling, a feature that reflects the f0 of a presented speech stimulus is predicted from neural EEG responses. The correlation between the predicted and the 'actual' f0 feature is a measure for neural response strength. In this study, we aimed to design a new f0 feature that approximates the expected human EEG response to the f0 in order to improve neural tracking results. Two techniques were explored: constructing the feature with a phenomenological model to simulate neural processing in the auditory periphery and low-pass filtering the feature to approximate the effect of more central processing. Analysis of EEG-data evoked by a Flemish story in 34 subjects indicated that both the auditory model and the low-pass filter significantly improved the correlations between the actual and reconstructed feature. The combination of both strategies almost doubled the mean correlation across subjects, from 0.078 to 0.13. Moreover, canonical correlation analysis revealed two distinct processes contributing to the f0 response: one driven by broad range of auditory nerve fibers with center frequency up to 8 kHz and one driven by a more narrow selection of auditory nerve fibers, possibly responding to unresolved harmonics. Optimizing the f0 feature towards the expected neural response, significantly improves f0-tracking correlations. The optimized f0 feature enhances the f0-tracking method, facilitating future research on temporal auditory processing in the human brain.