Neural tracking measures of speech intelligibility: Manipulating intelligibility while keeping acoustics unchanged

Neural speech tracking has advanced our understanding of how our brains rapidly map an acoustic speech signal onto linguistic representations and ultimately meaning. It remains unclear, however, how speech intelligibility is related to the corresponding neural responses. Many studies addressing this question vary the level of intelligibility by manipulating the acoustic waveform, but this makes it difficult to cleanly disentangle effects of intelligibility from underlying acoustical confounds. Here, using magnetoencephalography (MEG) recordings, we study neural measures of speech intelligibility by manipulating intelligibility while keeping the acoustics strictly unchanged. Acoustically identical degraded speech stimuli (three-band noise vocoded, ~20 s duration) are presented twice, but the second presentation is preceded by the original (non-degraded) version of the speech. This intermediate priming, which generates a ‘pop-out’ percept, substantially improves the intelligibility of the..., Magnetoencephalography (MEG) data were recorded from young adult participants as they listened to a passage of noise-vocoded speech, first before any priming, followed by listening to the original, non-degraded version of the same passage to invoke priming, and then finally listening to the same noise-vocoded speech passage as before. All information related to experimental procedure, stimuli, and preprocessing are described in the paper., , # Neural tracking measures of speech intelligibility: Manipulating intelligibility while keeping acoustics unchanged

[https://doi.org/10.5061/dryad.sbcc2frd6](https://doi.org/10.5061/dryad.sbcc2frd6)

The dataset includes raw MEG (magnetoencephalography) data, behavioral responses, stimuli, predictors, main codes, some intermediate results (Temporal response functions (TRFs), features extracted from TRFs), and statistical analysis codes.

## Description of the data and file structure

Important specific python packages are - eelbrain, mne, and trftools

**1. meg_control.zip** - Raw MEG data (.fiff) and empty room data (.fiff) for noise covariance, and transformation matrix for subjects in the control study
**2. meg_main1.zip, meg_main2.zip** - Raw MEG data (.fiff) and empty room data (.fiff) for noise covariance, and transformation matrix for subjects in the main study

The .fiff files are data recorded from MEG kit at the University of Maryland College Park (https://linguistics.umd.ed...