DocumentCode :
3699941
Title :
Feature selection and recognition of electroencephalogram signals: An extreme learning machine and genetic algorithm-based approach
Author :
Qin Lin;Jia-Bo Huang;Jian Zhong;Si-Da Lin;Yun Xue
Author_Institution :
School of Information Engineering, Guangdong Medical College, Dongguan, China, 523808
Volume :
2
fYear :
2015
fDate :
7/1/2015 12:00:00 AM
Firstpage :
499
Lastpage :
504
Abstract :
The effective recognition approach of the electroencephalogram (EEG) signals can significantly boost the performance and the development of the EEG-based diagnosis and treatment. A new approach which combines the Extreme Learning Machine (ELM) with the Genetic algorithm (GA) is proposed in this paper. In the proposed approach, the ELM is used both as the final classifier and the fitness function for the GA to select the optimal feature subset from the initial features extracted through time-frequency (TF) analysis. The GA is adopted as the complementary input optimization mechanism to improve the performance of the ELM. To testify the performance of the proposed approach, experiments were simulated using the real-world EEG signals of 2003 International BCI Competition dataset. The recognition results have proved the effectiveness of the proposed approach.
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2015 International Conference on
Type :
conf
DOI :
10.1109/ICMLC.2015.7340607
Filename :
7340607
Link To Document :
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