Neural Computation
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
An Introduction to Variational Methods for Graphical Models
Machine Learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Bayesian parameter estimation via variational methods
Statistics and Computing
Machine Learning: Discriminative and Generative (Kluwer International Series in Engineering and Computer Science)
The evidence framework applied to classification networks
Neural Computation
Inferring parameters and structure of latent variable models by variational bayes
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Review article: Human scalp EEG processing: Various soft computing approaches
Applied Soft Computing
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To develop effective learning algorithms for continuous prediction using Electro-encephalogram (EEG) signal is a challenging research issue in brain-computer interface (BCI). In this paper, we propose a unified framework based on variational Bayesian method to fully exploit information contained in BCI data. To make continuous prediction, trials in training data set are first divided into segments. There are two key issues here. The first is that the actual intention (label) at each time interval (segment) is unknown. The second is that the final decision of a whole trial has to be drawn based on the predictions at individual time intervals. To address these key issues together, we introduce two auxiliary distributions in the lower bound on the log posterior and maximize this lower bound based on variational Bayesian method. We evaluated the proposed method on three data sets of BCI competition 2003 and 2005. The experimental results show that the averaged accuracy of our method is among the best.