Development of a multimodal strategy to investigate speech processing mechanisms in infants

Abstract

Understanding how infants process speech signal is a question of interest in neuroscience, because it provides insight into the early language acquisition and cognition development. However, investigating these mechanisms requires neuroimaging tools capable of capturing brain activity, adapted to the specific challenges of measuring this population. This Bachelor’s thesis contributes to a broader project that aims to uncover speech processing mechanisms in 4-5-month-old infants, presenting the development of a multimodal strategy that integrates electroencephalography (EEG), functional Diffusion Correlation Spectroscopy (fDCS) and functional Near-Infrared Spectroscopy (fNIRS) to acquire data by characterizing the neuronal activity and its relation to oxygen metabolism (i.e., energetic demand and oxygen consumption). To do so, this project aims to develop a multimodal system that combines optical techniques (fNIRS and fDCS) with EEG. Optical techniques would provide hemodynamic response, while EEG will quantify the electrical activity of the neurons enabling acquisition with both high temporal and spatial resolution. To implement this approach, a custom pipeline combining Python with 3D modelling was developed to automate the generation of sensor configuration (EEG electrodes, fNIRS and fDCS optodes) while respecting anatomical and technical constraints. Moreover, it also addresses the challenge of reducing the attrition rate caused by poor signal quality due to hair related issues in fDCS measurements. To address this challenge, a novel mechanism was developed based on pressure adjustment. Following multiple iterations, some models were prototyped through 3D printing and CNC (Computer Numerical Control) turning to ensure mechanical robustness and usability. The final system has the potential to improve contact across diverse hair types, to reduce setup time and to minimize participant exclusion. While further improvements are needed before deployment, the system represents a meaningful step toward enabling simultaneous acquisition of EEG, fNIRS and fDCS data in early infancy.