Neural Computation
An introduction to variational methods for graphical models
Learning in graphical models
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Machine Learning: Discriminative and Generative (Kluwer International Series in Engineering and Computer Science)
Brain computer interfaces: a recurrent neural network approach
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
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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 in training data-set are first divided into segments. The difficulty is that the actual intention (label) at each time interval (segment) is unknown. To handle the uncertainty of the segment label, we treat the unknown labels as the hidden variables in the lower bound on the log posterior and maximize this lower bound via an EM-like algorithm. Experimental results have shown that the averaged accuracy of the proposed method is among the best.