Data for "Dynamics of brain-muscle networks reveal effects of age and somatosensory function on gait", iScience 2024.

Network data, 3D network trajectory data, and PCA data during overground walking in healthy young, healthy older and people with Parkinson's disease. Data were recorded via EEG from left and right sensorimotor cortices and surface EMG from eight leg muscles. We computed time-resolved intermuscular, cortico-muscular, and cortico-cortical coherence across the gait cycle and used orthogonal non-negative matrix factorization to extract brain-muscle networks that span a low-dimensional subspace. By projecting the temporal activations of these networks onto a low-dimensional subspace, we could assess the inter-subject variability in brain-muscle dynamics.

本数据集包含健康青年、健康老年及帕金森病患者平地行走时的网络数据、三维网络轨迹数据与主成分分析（PCA）数据。相关数据通过脑电图（EEG）采集自左右两侧感觉运动皮层（sensorimotor cortices），并通过表面肌电图（surface EMG）采集自8块腿部肌肉。本研究计算了步态周期内的时间分辨肌间、皮层-肌肉及皮层-皮层相干性，并采用正交非负矩阵分解（orthogonal non-negative matrix factorization）提取了位于低维子空间中的脑-肌肉网络。通过将这些网络的时间激活模式投影至低维子空间，本研究得以评估脑-肌肉动力学的个体间差异。