Functional connectivity between the brain and body kinematics has largely not been investigated due to the requirement of motionlessness in neuroimaging techniques such as functional magnetic resonance imaging (fMRI). However, this connectivity is disrupted in many neurodegenerative disorders, including Parkinsons Disease (PD), a neurological progressive disorder characterized by movement symptoms including slowness of movement, stiffness, tremors at rest, and walking and standing instability. In this study, brain activity is recorded through functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), and body kinematics were captured by a motion capture system (Mocap) based on an inertial measurement unit (IMU) for gross movements (large movements such as limb kinematics), and the WearUp glove for fine movements (small range movements such as finger kinematics). PD and neurotypical (NT) participants were recruited to perform 8 different movement tasks. The recorded data from each modality have been analyzed individually, and the processed data has been used for classification between the PD and NT groups. The average changes in oxygenated hemoglobin (HbO2) from fNIRS, EEG power spectral density in the Theta, Alpha, and Beta bands, acceleration vector from Mocap, and normalized WearUp flex sensor data were used for classification. 12 different support vector machine (SVM) classifiers have been used on different datasets such as only fNIRS data, only EEG data, hybrid fNIRS/EEG data, and all the fused data for two classification scenarios: classifying PD and NT based on individual activities, and all activity data fused together. The PD and NT group could be distinguished with more than 83% accuracy for each individual activity. For all the fused data, the PD and NT groups are classified with 81.23%, 92.79%, 92.27%, and 93.40% accuracy for the fNIRS only, EEG only, hybrid fNIRS/EEG, and all fused data, respectively. The results indicate that the overall performance of classification in distinguishing PD and NT groups improves when using both brain and body data.

中文翻译：

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合并fNIRS-EEG脑监测和身体动作捕捉以区分帕金森病

由于神经成像技术（例如功能磁共振成像（fMRI））对静止性的要求，因此很大程度上尚未研究大脑与身体运动学之间的功能连接性。但是，这种连通性在许多神经退行性疾病（包括帕金森病（PD））中被破坏，帕金森病（PD）是一种以运动症状为特征的神经系统进行性疾病，包括运动缓慢，僵硬，静止震颤以及行走和站立不稳。在这项研究中，通过功能近红外光谱（fNIRS）和脑电图（EEG）记录了大脑活动，并且运动捕捉系统（Mocap）基于惯性测量单元（IMU）捕获了总运动量（大），记录了人体运动学运动，例如肢体运动），和WearUp手套，可进行精细动作（小范围动作，例如手指运动学）。招募了PD和神经典型（NT）参与者来执行8种不同的运动任务。来自每个模态的记录数据已经过单独分析，处理过的数据已用于PD组和NT组之间的分类。来自fNIRS的含氧血红蛋白（HbO2）的平均变化，Theta，Alpha和Beta谱带的EEG功率谱密度，来自Mocap的加速度矢量以及归一化的WearUp flex传感器数据用于分类。在不同的数据集上使用了12种不同的支持向量机（SVM）分类器，例如仅fNIRS数据，仅EEG数据，fNIRS / EEG混合数据以及用于两种分类方案的所有融合数据：根据单个活动对PD和NT进行分类，并将所有活动数据融合在一起。对于每个单独的活动，PD和NT组的区分准确率均超过83％。对于所有融合数据，仅针对fNIRS，仅针对EEG，针对混合fNIRS / EEG和所有融合数据，PD和NT组的分类精度分别为81.23％，92.79％，92.27％和93.40％。结果表明，在同时使用大脑和身体数据时，分类在区分PD组和NT组方面的总体表现有所提高。