Investigating neural speech processing with functional near infrared spectroscopy: considerations for temporal response functions

Functional near infrared spectroscopy (fNIRS) is increasingly used in hearing and communication research, with advantages such as robustness to movement artifacts, improved spatial resolution, and flexibility of contexts in which it can be applied. At the same time, the field is progressively moving towards more continuous, naturalistic listening paradigms resulting in the widespread adoption of speech tracking analyses such as temporal response functions (TRFs) in electroencephalography (EEG) and magnetoencephalography (MEG) studies. However, it remains unclear whether these analyses can be applied to slower haemodynamic signals measured by fNIRS. In the present study, we investigated whether a TRF framework can similarly be applied to fNIRS data recorded during continuous speech perception. Eight participants listened to speech simultaneously while fNIRS signals were acquired in a hyperscanning setup. Speech features were regressed onto the haemodynamic responses to test the feasibility and interpretability of fNIRS-based TRFs. Prediction correlations between observed and modelled fNIRS signals across speech features were higher than those typically reported for EEG- and comparable to those reported for MEG-TRF studies. Moreover, these correlations did not overlap with a null distribution generated from triallJmismatched fNIRS data, confirming statistical significance and were slightly greater than those obtained from a conventional GLM approach. Our findings support that TRF estimation method can yield meaningful and statistically significant responses from fNIRS data. HighlightsO_LITRF modelling can be meaningfully applied to fNIRS data acquired during speech listening tasks. C_LIO_LIPrediction correlations between actual and modelled fNIRS signals were above chance level, with values comparable to previous EEG/MEG studies. C_LIO_LITRFs explained more fNIRS variance than a conventional GLM approach. C_LI