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...神经言语追踪（Neural speech tracking）技术推动了我们对大脑如何快速将声学言语信号映射至语言表征并最终通达语义的认知。然而，言语可懂度与对应神经响应之间的关联机制仍未明确。诸多针对该问题的研究通过操控声学波形来调整言语可懂度水平，但这使得研究者难以清晰地将可懂度的效应与潜在声学混淆因素分离开来。本研究借助脑磁图（magnetoencephalography, MEG）记录，通过在严格保持声学特性不变的前提下操控言语可懂度，探究言语可懂度的神经表征。声学特性完全一致的劣化言语刺激（三频段噪声声码化处理，时长约20秒）会被呈现两次，但第二次呈现前会先播放该言语的原始（未劣化）版本。这种中间启动范式可引发“突现感知”（pop-out percept），能够显著提升该……的言语可懂度。 研究过程中，我们对青年被试进行脑磁图记录：被试首先聆听一段噪声声码化言语，无任何启动条件；随后聆听同段落的原始未劣化版本以启动认知；最后再次聆听此前的噪声声码化言语段落。与实验流程、刺激材料及预处理相关的全部细节均已在论文中阐明。 # 言语可懂度的神经追踪表征：在保持声学特性不变的前提下操控可懂度 [https://doi.org/10.5061/dryad.sbcc2frd6](https://doi.org/10.5061/dryad.sbcc2frd6) 本数据集包含原始脑磁图（magnetoencephalography, MEG）数据、行为反应数据、刺激材料、预测变量、核心代码、部分中间结果（时域响应函数（Temporal response functions, TRFs）及从TRFs中提取的特征），以及统计分析代码。 ## 数据与文件结构说明 所需核心Python包包括：eelbrain、mne及trftools。 **1. meg_control.zip**：对照研究中被试的原始脑磁图数据（.fiff格式）、用于计算噪声协方差的空室数据（.fiff格式），以及变换矩阵。 **2. meg_main1.zip、meg_main2.zip**：主研究中被试的原始脑磁图数据（.fiff格式）、用于计算噪声协方差的空室数据（.fiff格式），以及变换矩阵。 .fiff格式文件为马里兰大学帕克分校脑磁仪采集的数据（https://linguistics.umd.ed...）