Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Advances in fuzzy integration for pattern recognition
Fuzzy Sets and Systems - Special issue on fuzzy methods for computer vision and pattern recognition
Joint time-frequency-space classification of EEG in a brain-computer interface application
EURASIP Journal on Applied Signal Processing
Data dependence in combining classifiers
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
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In this paper we study the effectiveness of using multiple classifier combination for EEG signals classification aiming to obtain more accurate results than it possible from single classifier system. The developed system employs different features vectors fused at the abstract and measurement levels for integrating information to reach a collective decision. For making decision, the majority voting scheme has been used. While at the measurement level, fuzzy integral, majority vote, decision template and some other types of combination methods have been investigated. The ensemble classification task is completed by feeding the Support Vectors Machines with Redial Basis Kernel functions classifiers with different features extracted from the EEG signal for imagination of right and left hands movements (i.e., at EEG channels C3 and C4). The parameters of SVM classifiers were optimized by genetic algorithm. The results show that using classifier fusion methods improved the overall classification performance.