Statistical Approaches to Estimating the Number of Signal Sources in Magnetoencephalography

来源 :上海交通大学 | 被引量 : 0次 | 上传用户:lcqinyuyang
下载到本地 , 更方便阅读
声明 : 本文档内容版权归属内容提供方 , 如果您对本文有版权争议 , 可与客服联系进行内容授权或下架
论文部分内容阅读
  Magnetoencephalography(MEG)is an imaging technique used to measure the magnetic field outside the human head produced by the electrical activity inside the brain.
其他文献
The Ginibre point process has attracted considerable attention recently because of its wide use in modelling mobile networks.
There is an increasing interest in moving cognitive studies beyond the identification of anatomical locations of functional processes that are time-locked to a particular task,to the detection of the
Its theoretically desirable to perform variable selection via penalized likelihood that directly penalizes the number of variables in generalized linear models(L0 penalized methods).
In this talk,I will discuss a nonlinear mixed-effects scalar-on-function regression model using a Gaussian process prior.
Put the body of your abstract here.We consider to estimate community network induced large correlation matrices.
Typically in clinical trials,outcomes of interested are repeatedly measured over time.A good analysis method or decision making procedure should be objective and try to utilize all available informati
Global submission with data from MRCTs is a normal practice,particularly for big outcome studies,rare disease,or disease with high unmet medical need etc.
Central composite designs are widely used in practice for factor screening and building response surface models.
We propose new,optimal methods for analyzing randomized trials,when it is suspected that treatment effects may differ in two predefined subpopulations.
Multiple correlated phenotypes are frequently collected in genome-wide association studies(GWASs),and a systematic,simultaneous analysis of multiple phenotypes can integrate the signals from single ph