arXiv subjects

Condell Eastmond

Publications and source records attributed to Condell Eastmond.

2 recordsLinked to original sources

arXiv2026

High-Fidelity 3D Simulator for Synthetic fNIRS Data Generation

Functional near-infrared spectroscopy (fNIRS) provides a noninvasive window into brain activity by measuring task-related changes in oxygenated and deoxygenated hemoglobin in the cortex. A key advantage of fNIRS is its promise of use with mobile participants in complex, real-world environments, such as walking, sports, classroom learning, driving simulations, or social interactions. However, analyzing fNIRS data is challenging because of motion artifacts, physiological noise, and other confounding factors. This challenge is further compounded by the limited availability of annotated datasets, which hinders the development and validation of new analysis pipelines, particularly given the growing use of AI methods. Recognizing these challenges, we introduce a 3D fNIRS simulator that uses mesh-based Monte Carlo simulations to create physiologically realistic, full-head synthetic recordings with high spatiotemporal fidelity. Our simulator combines anatomically accurate sensitivity profiles with parameterized models of hemodynamic responses, systemic physiology, and nonsystematic artifacts. As a result, users can generate virtually unlimited labeled datasets for testing denoising algorithms, data augmentation, mechanistic modeling, or \textit{in silico} experimentation. We validate the simulator using experimental fNIRS data from open-source finger-tapping, pain-assessment, and surgical-skill datasets and provide an open-source implementation to support reproducibility and broad adoption.

q-bio.NC↗

arXiv2022

Deep Learning in fNIRS: A review

Significance: Optical neuroimaging has become a well-established clinical and research tool to monitor cortical activations in the human brain. It is notable that outcomes of functional Near-InfraRed Spectroscopy (fNIRS) studies depend heavily on the data processing pipeline and classification model employed. Recently, Deep Learning (DL) methodologies have demonstrated fast and accurate performances in data processing and classification tasks across many biomedical fields. Aim: We aim to review the emerging DL applications in fNIRS studies. Approach: We first introduce some of the commonly used DL techniques. Then the review summarizes current DL work in some of the most active areas of this field, including brain-computer interface, neuro-impairment diagnosis, and neuroscience discovery. Results: Of the 63 papers considered in this review, 32 report a comparative study of deep learning techniques to traditional machine learning techniques where 26 have been shown outperforming the latter in terms of classification accuracy. Additionally, 8 studies also utilize deep learning to reduce the amount of preprocessing typically done with fNIRS data or increase the amount of data via data augmentation. Conclusions: The application of DL techniques to fNIRS studies has shown to mitigate many of the hurdles present in fNIRS studies such as lengthy data preprocessing or small sample sizes while achieving comparable or improved classification accuracy.

q-bio.NC↗