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.本数据集涵盖健康青年、健康老年人群及帕金森病（Parkinson's disease）患者平地行走过程中的网络数据、三维网络轨迹数据与主成分分析（Principal Component Analysis, PCA）数据。我们通过脑电图（Electroencephalogram, EEG）采集双侧感觉运动皮层的脑电信号，并通过表面肌电图（surface electromyography, EMG）采集8块腿部肌肉的肌电信号。我们计算了步态周期内的时分辨肌间相干性、皮层-肌相干性及皮层-皮层相干性，并采用正交非负矩阵分解提取跨越低维子空间的脑肌网络。通过将上述脑肌网络的时序激活模式投影至低维子空间，我们能够评估脑肌动力学的个体间差异。