Advancing fNIRS Neuroimaging through Synthetic Data Generation and Machine Learning Applications

TL;DR

This paper introduces a comprehensive framework combining synthetic data generation and machine learning to enhance fNIRS neuroimaging, addressing data scarcity and improving analysis accuracy and scalability.

Contribution

It presents a novel integrated approach using Monte Carlo simulations, containerized environments, and cloud infrastructure for scalable, reproducible fNIRS data analysis.

Findings

Generated diverse synthetic neuroimaging datasets

Established scalable cloud-based data processing pipeline

Improved machine learning model performance for fNIRS analysis

Abstract

This study presents an integrated approach for advancing functional Near-Infrared Spectroscopy (fNIRS) neuroimaging through the synthesis of data and application of machine learning models. By addressing the scarcity of high-quality neuroimaging datasets, this work harnesses Monte Carlo simulations and parametric head models to generate a comprehensive synthetic dataset, reflecting a wide spectrum of conditions. We developed a containerized environment employing Docker and Xarray for standardized and reproducible data analysis, facilitating meaningful comparisons across different signal processing modalities. Additionally, a cloud-based infrastructure is established for scalable data generation and processing, enhancing the accessibility and quality of neuroimaging data. The combination of synthetic data generation with machine learning techniques holds promise for improving the accuracy, efficiency, and applicability of fNIRS tomography, potentially revolutionizing diagnostics and treatment strategies for neurological conditions. The methodologies and infrastructure developed herein set new standards in data simulation and analysis, paving the way for future research in neuroimaging and the broader biomedical engineering field.