Spatial Harmonic Analysis of EEG Data

Bayesian Prediction in Spatial Data Analysis

Spherical harmonic decomposition applied to spatial-temporal analysis of human high-density EEG

We demonstrate an application of spherical harmonic decomposition to analysis of the human electroencephalogram (EEG). We implement two methods and discuss issues specific to analysis of hemispherical, irregularly sampled data. Performance of the methods and spatial sampling requirements are quantified using simulated data. The analysis is applied to experimental EEG data, confirming earlier re...

Bayesian Analysis of Survival Data with Spatial Correlation

Often in practice the data on the mortality of a living unit correlation is due to the location of the observations in the study‎. ‎One of the most important issues in the analysis of survival data with spatial dependence‎, ‎is estimation of the parameters and prediction of the unknown values in known sites based on observations vector‎. ‎In this paper to analyze this type of survival‎, ‎Cox...

Determination of Optimal Sampling Design for Spatial Data Analysis

Extended Abstract. Inferences for spatial data are affected substantially by the spatial configuration of the network of sites where measurements are taken. Consider the following standard data-model framework for spatial data. Suppose a continuous, spatially-varying quantity, Z, is to be observed at a predetermined number, n, of points ....[ To Countinue Click here]

Non-stationary Spatial Modeling Using Harmonic Analysis

Since in most applications of geostatistics only one realization of the random phenomenon can be observed, it is common standard to make the assumption of stationarity. Recently, motivated by applications in environmental and atmospheric monitoring, where the investigated phenomena act on a very large scale, new non-stationary covariance function models, estimation and prediction methods have b...