A Novel Optimization Framework for fNIRS: Enhancing Brain Image Reconstruction for Neurorehabilitation

Abstract

Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technique that provides valuable insights into brain activity by measuring haemodynamic changes in blood oxygenation. Despite its potential, the accuracy of fNIRS-based brain image reconstruction is often compromised by motion artefacts. While conventional adaptive signal processing methods can be employed to remove these artefacts, neural networks have demonstrated a more effective alternative. However, traditional neural networks typically have substantial computational and memory requirements, making them unsuitable for resource-constrained wearable platforms. In this study, we propose an optimisation framework for neural network processing specifically designed for wearable devices to enhance the clarity and reliability of fNIRS brain images. Through systematic evaluation and integrate of various datasets on resource limited computing platform, we establish a standardised a standardised proceeding pipeline that can be applied across various fNIRS datasets. The proposed framework is validated on three datasets, demonstrating significant improvements in signal quality and image reconstruction accuracy, while achieving a 24% reduction in memory footprint optimisation. Our findings suggest that adopting a universal preprocessing optimisation strategy could standardise fNIRS data analysis for wearable devices, enabling more consistent and interpretable results across studies. This advancement contributes to the broader application of fNIRS in clinical and neurorehabilitation research, make real-time neuroimaging more feasible and effective.