A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
On-line EM Algorithm for the Normalized Gaussian Network
Neural Computation
The self-paced Graz brain-computer interface: methods and applications
Computational Intelligence and Neuroscience - EEG/MEG Signal Processing
Computational Intelligence and Neuroscience - Brain-Computer Interfaces: Towards Practical Implementations and Potential Applications
Sequential EM for Unsupervised Adaptive Gaussian Mixture Model Based Classifier
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
A hybrid approach to feature subset selection for brain-computer interface design
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Interfaces cérebro-computador de sistemas interativos: estado da arte e desafios de IHC
Proceedings of the 11th Brazilian Symposium on Human Factors in Computing Systems
BCI could make old two-player games even more fun: a proof of concept with "connect four"
Advances in Human-Computer Interaction - Special issue on Using Brain Waves to Control Computers and Machines
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This paper presents a novel user interface suitable for adaptive Brain Computer Interface (BCI) system. A customized self-paced BCI architecture is introduced where the system combines onset detection system along with an adaptive classifier working in parallel. An unsupervised adaptive method based on sequential expectation maximization for Gaussian mixture model is employed with new timing scheme and an additional averaging step to avoid over-fitting. Sigmoid function based post-processing approach is proposed to enhance the classifiers' output. The adaptive system is compared to a non-adaptive one and tested on five subjects who used the BCI to play the hangman game. The results show significant improvement of the True-False difference for all the classes and a reduction in the number of steps required to solve the problem.