Di Liberto Gm

Bio: Di Liberto Gm is an academic researcher. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

Linear Modeling of Neurophysiological Responses to Naturalistic Stimuli: Methodological Considerations for Applied Research

Abstract: Cognitive neuroscience has seen an increase in the use of linear modelling techniques for studying the processing of natural, environmental stimuli. The availability of such computational tools has prompted similar investigations in many clinical domains, facilitating the study of cognitive and sensory deficits within an ecologically relevant context. However, studying clinical (and often highly-heterogeneous) cohorts introduces an added layer of complexity to such modelling procedures, leading to an increased risk of improper usage of such techniques and, as a result, inconsistent conclusions. Here, we outline some key methodological considerations for applied research and include worked examples of both simulated and empirical electrophysiological (EEG) data. In particular, we focus on experimental design, data preprocessing and stimulus feature extraction, model design, training and evaluation, and interpretation of model weights. Throughout the paper, we demonstrate how to implement each stage in MATLAB using the mTRF-Toolbox and discuss how to address issues that could arise in applied cognitive neuroscience research. In doing so, we highlight the importance of understanding these more technical points for experimental design and data analysis, and provide a resource for applied and clinical researchers investigating sensory and cognitive processing using ecologically-rich stimuli.

Occlusion of lip movements impairs reconstruction of acoustic speech features and higher-level segmentational features in the presence of a distractor speaker

Abstract: Multisensory integration enables stimulus representation even when the sensory input in a single modality is weak. In the context of speech, when confronted with a degraded acoustic signal, congruent visual inputs promote comprehension. When this input is occluded speech comprehension consequently becomes more difficult. But it still remains inconclusive which levels of speech processing are affected under which circumstances by occlusion of the mouth area. To answer this question, we conducted an audiovisual (AV) multi-speaker experiment using naturalistic speech. In half of the trials, the target speaker wore a (surgical) face mask, while we measured the brain activity of normal hearing participants via magnetoencephalography (MEG). We additionally added a distractor speaker in half of the trials in order to create an ecologic difficult listening situation. A decoding model on the clear AV speech was trained and used to reconstruct crucial speech features in each condition. We found significant main effects of face masks on the reconstruction of acoustic features, such as the speech envelope and spectral speech features (i.e. pitch and formant frequencies), while reconstruction of higher level features of speech segmentation (phoneme and word onsets) were especially impaired through masks in difficult listening situations. As we used surgical face masks in our study, which only show mild effects on speech acoustics, we interpret our findings as the result of the occluded lip movements. This idea is in line with recent research showing that visual cortical regions track spectral modulations. Our findings extend previous behavioural results, by demonstrating the complex contextual effects of occluding relevant visual information on speech processing.

Predictors for Estimating Subcortical EEG Responses to Continuous Speech

Abstract: Perception of sounds and speech involves structures in the auditory brainstem that rapidly process ongoing auditory stimuli. The role of these structures in speech understanding can be investigated by measuring their electrical activity using scalpmounted electrodes. Typical analysis methods involve averaging responses to many short repetitive stimuli. Recently, responses to more ecologically relevant continuous speech were detected using linear encoding models called temporal response functions (TRFs). Non-linear predictors derived from complex auditory models may improve TRFs. Here, we compare predictors from both simple and complex auditory models for estimating brainstem TRFs on electroencephalography (EEG) data from 24 subjects listening to continuous speech. Predictors from simple models result in comparable TRFs to those from complex models, and are much faster to compute. We also discuss the effect of data length on TRF peaks for efficient estimation of subcortical TRFs.