On-line learning in neural networks
On-line learning in neural networks
Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Artificial Intelligence
Non-invasive brain-actuated control of a mobile robot
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
An experimental evaluation of ensemble methods for EEG signal classification
Pattern Recognition Letters
The random electrode selection ensemble for EEG signal classification
Pattern Recognition
The random electrode selection ensemble for EEG signal classification
Pattern Recognition
Ensemble learning methods for classifying EEG signals
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Clustering technique-based least square support vector machine for EEG signal classification
Computer Methods and Programs in Biomedicine
Trial pruning based on genetic algorithm for single-trial EEG classification
Computers and Electrical Engineering
Robust learning of mixture models and its application on trial pruning for EEG signal analysis
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
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The classification of time-varying neurophysiological signals, e.g., electroencephalogram (EEG) signals, advances the requirement of adaptability for classifiers. In this paper we address the challenge of neurophysiological signal classification arising from brain-computer interface (BCI) applications and propose an on-line classifier designed via the decorrelated least mean square (LMS) algorithm. Based on a Bayesian classifier with Gaussian mixture models, we derive the general formulation of gradient descent algorithms under the criterion of LMS. Further, to accelerate convergence, the decorrelated gradient instead of the instantaneous gradient is adopted for updating the parameters of the classifier adaptively. Utilizing the presented classifier for the off-line analysis of practical classification tasks in brain-computer interface applications shows its effectiveness and robustness compared to the stochastic gradient descent classifier which uses the instantaneous gradient directly.