首页> 外文期刊>NeuroImage >Dynamics underlying spontaneous human alpha oscillations: a data-driven approach.
【24h】

Dynamics underlying spontaneous human alpha oscillations: a data-driven approach.

机译:自发人类阿尔法振荡的潜在动力学:一种数据驱动的方法。

获取原文
获取原文并翻译 | 示例
       

摘要

Although the cognitive and clinical correlates of spontaneous human alpha oscillations as recorded with electroencephalography (EEG) or magnetoencephalography (MEG) are well documented, the dynamics underlying these oscillations is still a matter of debate. This study proposes a data-driven method to reveal the dynamics of these oscillations. It demonstrates that spontaneous human alpha oscillations as recorded with MEG can be viewed as noise-perturbed damped harmonic oscillations. This provides evidence for the hypothesis that these oscillations reflect filtered noise and hence do not possess limit-cycle dynamics. To illustrate the use of the model, we apply it to two data-sets in which a decrease in alpha power can be observed across conditions. The associated differences in the estimated model parameters show that observed decreases in alpha power are associated with different kinds of changes in the dynamics. Thus, the model parameters are useful dynamical biomarkers for spontaneous human alpha oscillations.
机译:尽管用脑电图(EEG)或磁脑电图(MEG)记录的自发性人类α振荡的认知和临床相关性已得到充分证明,但这些振荡背后的动力学仍是一个有争议的问题。这项研究提出了一种数据驱动的方法来揭示这些振荡的动力学。它证明了用MEG记录的自发人类α振荡可以看作是噪声干扰的阻尼谐波振荡。这为以下假设提供了证据:这些振荡反映了滤波后的噪声,因此不具有极限循环动力学。为了说明模型的使用,我们将其应用于两个数据集,在这些数据集中可以观察到整个条件下α功率的降低。估计的模型参数的相关差异表明,观察到的α功率下降与动力学的不同变化相关。因此,模型参数对于自发性人类阿尔法振荡是有用的动力学生物标记。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号