The dataset for the Electrospinography (ESG) for non-invasively recording spinal sensorimotor networks in humans project

Objective. Currently, few non-invasive measures exist for directly measuring spinal sensorimotor networks. Electrospinography (ESG) is one non-invasive method but is primarily used to measure evoked responses or for monitoring the spinal cord during surgery. Our objectives for collecting this dataset were to evaluate the feasibility of ESG to measure spinal sensorimotor networks by determining spatiotemporal and functional connectivity changes during single-joint movements at the spinal and cortical levels.Approach. We synchronously recorded electroencephalography (EEG), electrooculography (EOG), ESG, electromyography (EMG), and lower-limb kinematics using inertial movement units (IMUs) in ten neurologically intact adults while performing one of three lower-limb tasks (no movement, plantar-flexion and knee flexion) in the prone position. Results. We share the multimodal dataset. Findings are reported in the manuscript referenced below.Significance. To our knowledge this is the first dataset using high density ESG, EEG, EOG, EMG and IMUs for characterizing lower limb movements. The dataset could be used for investigating ESG decoding of efferent and afferent signaling in neurologically intact adults and other applications.

研究目标。目前，直接测量脊柱感觉运动网络的非侵入性方法寥寥无几。电肌电图（ESG）是一种非侵入性检测手段，但其主要用途在于测量诱发电位或手术中脊髓的监控。本数据集的收集旨在评估ESG在测量脊柱感觉运动网络方面的可行性，通过确定单关节运动过程中脊柱和皮质层面的时空及功能连接变化来实现。研究方法。我们同步记录了10位神经功能正常成人在俯卧位进行三种下肢任务（无运动、足底屈曲和膝关节屈曲）时的脑电图（EEG）、眼电描记图（EOG）、电肌电图（ESG）、肌电图（EMG）和下肢运动学数据，使用惯性运动单元（IMUs）。研究结果。我们分享了多模态数据集。相关发现已报道在以下引用的论文中。研究意义。据我们所知，这是首个利用高密度ESG、EEG、EOG、EMG及IMUs来表征下肢运动的数据库。该数据集可用于研究神经功能正常成人的ESG解码输出和输入信号，以及其他应用。

搜集汇总

背景与挑战

背景概述

该数据集旨在评估电脊图（ESG）作为非侵入性方法测量脊髓感觉运动网络的可行性，包含来自10名健康成人的多模态同步记录数据，如EEG、EMG、ESG和IMU，覆盖下肢运动任务。数据集结构清晰，支持神经工程和生物医学研究，重点关注脊髓与皮层活动的时空和功能连接分析。