FEATURE SELECTION AND RECOGNITION OF ELECTROENCEPHALOGRAM SIGNALS: AN EXTREME LEARNING MACHINE AND GENETIC ALGORITHM-BASED APPROACH

被引：0
|
作者：
Lin, Qin [1 ]
Huang, Jia-Bo [2 ]
Zhong, Jian [1 ]
Lin, Si-Da [1 ]
Xue, Yun [2 ]
机构：
[1] Guangdong Med Coll, Sch Informat Engn, Dongguan 523808, Peoples R China
[2] S China Normal Univ, Sch Phys & Telecommun Engn, Guangzhou 510006, Guangdong, Peoples R China
来源：
PROCEEDINGS OF 2015 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOL. 2 | 2015年
关键词：
Brain-Computer Interface (BCI); Electroencephalogram (EEG); Genetic algorithm (GA); Extreme Learning Machine (ELM); Artificial Neural Network (ANN); BCI COMPETITION 2003; CLASSIFICATION;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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 haw proved the effectiveness of the proposed approach.
引用
收藏
页码：499 / 504
页数：6
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