Ferré Iara Beatriz Silva, Corso Gilberto, Dos Santos Lima Gustavo Zampier, Lopes Sergio Roberto, Leocadio-Miguel Mario André, França Lucas G S, de Lima Prado Thiago, Araújo John Fontenele
Programa de Pós-Graduação em Psicobiologia, Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil.
Departamento de Biofísica e Farmacologia, Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil.
PLoS One. 2024 Oct 2;19(10):e0298703. doi: 10.1371/journal.pone.0298703. eCollection 2024.
Brain Complexity (BC) have successfully been applied to study the brain electroencephalographic signal (EEG) in health and disease. In this study, we employed recurrence entropy to quantify BC associated with the neurophysiology of movement by comparing BC in both resting state and cycling movement. We measured EEG in 24 healthy adults and placed the electrodes on occipital, parietal, temporal and frontal sites on both the right and left sides of the brain. We computed the recurrence entropy from EEG measurements during cycling and resting states. Entropy is higher in the resting state than in the cycling state for all brain regions analysed. This reduction in complexity is a result of the repetitive movements that occur during cycling. These movements lead to continuous sensorial feedback, resulting in reduced entropy and sensorimotor processing.
脑复杂性（BC）已成功应用于研究健康和疾病状态下的脑电信号（EEG）。在本研究中，我们通过比较静息状态和骑行运动时的BC，采用递归熵来量化与运动神经生理学相关的BC。我们对24名健康成年人进行了EEG测量，并将电极放置在大脑左右两侧的枕叶、顶叶、颞叶和额叶部位。我们计算了骑行和静息状态下EEG测量的递归熵。在所分析的所有脑区中，静息状态下的熵高于骑行状态。这种复杂性的降低是骑行过程中重复运动的结果。这些运动导致持续的感觉反馈，从而使熵降低和感觉运动处理减少。
