Ensemble learning methods for classifying EEG signals

Authors:
Shiliang Sun
Affiliations:
Department of Computer Science and Technology, East China Normal University, Shanghai, China
Venue:
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Year:
2007

Citing 11
Cited 0

Bagging predictors

Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting

Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
The Random Subspace Method for Constructing Decision Forests

IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization

Machine Learning
Random Forests

Machine Learning
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants

Machine Learning
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Brain-actuated interaction

Artificial Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Learning on-line classification via decorrelated LMS algorithm: application to brain-computer interfaces

DS'05 Proceedings of the 8th international conference on Discovery Science
Asymmetric hemisphere modeling in an offline brain-computerinterface

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

Quantified Score

Hi-index	0.00

Visualization

Abstract

Bagging, boosting and random subspace are three popular ensemble learning methods, which have already shown effectiveness in many practical classification problems. For electroencephalogram (EEG) signal classification arising in recent brain-computer interface (BCI) research, however, there are almost no reports investigating their feasibilities. This paper systematically evaluates the performance of these three ensemble methods for their new application on EEG signal classification. Experiments are conducted on three BCI subjects with k-nearest neighbor and decision tree as base classifiers. Several valuable conclusions are derived about the feasibility and performance of ensemble methods for classifying EEG signals.