一种新的fNIRS优化框架:增强脑图像重建用于神经康复

IF 5.2 2区 医学 Q2 ENGINEERING, BIOMEDICAL
Yunyi Zhao;Uwe Dolinsky;Hubin Zhao;Shufan Yang
{"title":"一种新的fNIRS优化框架:增强脑图像重建用于神经康复","authors":"Yunyi Zhao;Uwe Dolinsky;Hubin Zhao;Shufan Yang","doi":"10.1109/TNSRE.2025.3602894","DOIUrl":null,"url":null,"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.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3409-3420"},"PeriodicalIF":5.2000,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11142360","citationCount":"0","resultStr":"{\"title\":\"A Novel Optimization Framework for fNIRS: Enhancing Brain Image Reconstruction for Neurorehabilitation\",\"authors\":\"Yunyi Zhao;Uwe Dolinsky;Hubin Zhao;Shufan Yang\",\"doi\":\"10.1109/TNSRE.2025.3602894\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":13419,\"journal\":{\"name\":\"IEEE Transactions on Neural Systems and Rehabilitation Engineering\",\"volume\":\"33 \",\"pages\":\"3409-3420\"},\"PeriodicalIF\":5.2000,\"publicationDate\":\"2025-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11142360\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Neural Systems and Rehabilitation Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11142360/\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11142360/","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0

摘要

功能性近红外光谱(fNIRS)是一种非侵入性神经成像技术,通过测量血氧的血流动力学变化,为大脑活动提供了有价值的见解。尽管具有潜力,但基于fnir的脑图像重建的准确性经常受到运动伪影的影响。虽然传统的自适应信号处理方法可以用来去除这些伪影,但神经网络已经证明了一种更有效的替代方法。然而,传统的神经网络通常具有大量的计算和内存需求,这使得它们不适合资源有限的可穿戴平台。在这项研究中,我们提出了一个专门为可穿戴设备设计的神经网络处理优化框架,以提高近红外脑图像的清晰度和可靠性。通过在资源有限的计算平台上对各种数据集进行系统的评估和整合,我们建立了一个标准化的、可应用于各种近红外数据集的标准化处理流程。提出的框架在三个数据集上进行了验证,证明了信号质量和图像重建精度的显着改善,同时实现了内存占用优化减少24%。我们的研究结果表明,采用通用的预处理优化策略可以标准化可穿戴设备的近红外光谱数据分析,使研究结果更加一致和可解释。这一进展有助于fNIRS在临床和神经康复研究中的广泛应用,使实时神经成像更加可行和有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Optimization Framework for fNIRS: Enhancing Brain Image Reconstruction for Neurorehabilitation
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
8.60
自引率
8.20%
发文量
479
审稿时长
6-12 weeks
期刊介绍: Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信