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Updated: Jul 19, 2025

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

Published on: December 2, 2015

在老年人中基于深度学习的行走任务分类使用fNIRS

Dongning Ma, Meltem Izzetoglu, Roee Holtzer

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

|August 18, 2023

概括

此摘要是机器生成的。

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这项研究引入了一种深度学习方法,在老年人中自动分类行走时的注意力状态. 该方法准确地区分了低和高注意力状态,有助于评估跌倒风险.

科学领域:

神经科学是一个神经科学.
生物医学工程生物医学工程
老年学是一门学科.

背景情况:

老年人的步行能力下降与增加的残疾和死亡率有关.
双重任务行走 (DTW) 加剧了步态和认知缺陷,与跌倒史相关.
在DTW期间,前额叶皮层 (PFC) 的皮质活动可以通过功能近红外光谱学 (fNIRS) 进行测量.

研究的目的:

开发一个自动分类系统,以低 (单任务行走 - STW) 和高 (DTW) 的注意力行走状态在老年人中.
利用深度学习来分类不同行走条件下的认知激活.
通过结合fNIRS数据,性别和认知状态来提高分类准确性.

主要方法:

制定了STW作为低注意力状态和DTW作为高注意力状态.
分析了fNIRS数据,重点关注HbO2和Hb值之间的差异作为特征.
工程 fNIRS 功能为 3 道图像和应用数据增强用于深度学习模型.
精心调整的预先训练的深度学习模型与fNIRS数据集,性别和认知状态.

主要成果:

实现了大约81%的分类准确度,超过传统机器学习的~10%.
证明了使用HbO2 - Hb作为输入图像中的第三通道的有效性.

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fMRI Validation of fNIRS Measurements During a Naturalistic Task

fMRI Validation of fNIRS Measurements During a Naturalistic Task

Published on: June 15, 2015

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Last Updated: Jul 19, 2025

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

Published on: December 2, 2015

Revised and Neuroimaging-Compatible Versions of the Dual Task Screen

Revised and Neuroimaging-Compatible Versions of the Dual Task Screen

Published on: October 5, 2020

fMRI Validation of fNIRS Measurements During a Naturalistic Task

fMRI Validation of fNIRS Measurements During a Naturalistic Task

Published on: June 15, 2015

确定使用预训练模型和所有voxel位置产生最佳分类性能.

结论:

深度学习模型可以有效地使用fNIRS数据对老年人行走时的注意力状态进行分类.
拟议的方法为客观评估步行期间的认知负载提供了一个有希望的方法.
这些发现支持了改善落风险预测和干预策略的潜力.