Expectation-Maximization Method for EEG-Based Continuous Cursor Control

Expectation-Maximization Method for EEG-Based Continuous Cursor Control

To develop effective learning algorithms for continuous prediction of cursor movement using EEG signals is a challenging research issue in brain-computer interface (BCI). In this paper, we propose a novel statistical approach based on expectation-maximization (EM) method to learn the parameters of a classifier for EEG-based cursor control. To train a classifier for continuous prediction, trials...

Reconstruction of Pet Image Based on Kernelized Expectation-maximization Method

Positron emission tomography (PET) image reconstruction is challenging for low count frame. The reconstruction is important method to retrieve information that has been lost in the images. To improve image quality, prior information are used. Based on kernel method, PET image intensity in each pixel is obtained from prior information and the coefficients can be estimated by the maximum likeliho...

Spectrometer Calibration by Expectation Maximization Method

Expectation Maximization (EM) algorithm is a parameter estimation method from incomplete observations. In this paper, an implementation of this method to the calibration of HKS spectrometer at Jefferson Lab is described. We show that the application of EM method is able to calibrate the spectrometer properly in the presence of high background noise, while the traditional nonlinear Least Square ...

Expectation Maximization

Expectation Maximization

The Expectation Maximization (EM) algorithm [1, 2] is one of the most widely used algorithms in statistics. Suppose we are given some observed data X and a model family parametrized by θ, and would like to find the θ which maximizes p(X |θ), i.e. the maximum likelihood estimator. The basic idea of EM is actually quite simple: when direct maximization of p(X |θ) is complicated we can augment the...