An Optimal Transformation for Discriminant and Principal Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature selection for ensembles
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Ensemble Feature election with the Simple Bayesian Classification in Medical Diagnostics
CBMS '02 Proceedings of the 15th IEEE Symposium on Computer-Based Medical Systems (CBMS'02)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
An experimental evaluation of ensemble methods for EEG signal classification
Pattern Recognition Letters
Using random subspace to combine multiple features for face recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
DS'05 Proceedings of the 8th international conference on Discovery Science
Weighting Individual Classifiers by Local Within-Class Accuracies
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Ensembles of Feature Subspaces for Object Detection
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Local within-class accuracies for weighting individual outputs in multiple classifier systems
Pattern Recognition Letters
Extreme energy difference for feature extraction of EEG signals
Expert Systems with Applications: An International Journal
Multiple-view multiple-learner active learning
Pattern Recognition
Review article: Human scalp EEG processing: Various soft computing approaches
Applied Soft Computing
Clustering technique-based least square support vector machine for EEG signal classification
Computer Methods and Programs in Biomedicine
International Journal of Mobile Learning and Organisation
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Pattern classification methods are a crucial direction in the current study of brain-computer interface (BCI) technology. A simple yet effective ensemble approach for electroencephalogram (EEG) signal classification named the random electrode selection ensemble (RESE) is developed, which aims to surmount the instability demerit of the Fisher discriminant feature extraction for BCI applications. Through the random selection of recording electrodes answering for the physiological background of user-intended mental activities, multiple individual classifiers are constructed. In a feature subspace determined by a couple of randomly selected electrodes, principal component analysis (PCA) is first used to carry out dimensionality reduction. Successively Fisher discriminant is adopted for feature extraction, and a Bayesian classifier with a Gaussian mixture model (GMM) approximating the feature distribution is trained. For a test sample the outputs from all the Bayesian classifiers are combined to give the final prediction for its label. Theoretical analysis and classification experiments with real EEG signals indicate that the RESE approach is both effective and efficient.