Application of Swarm Intelligence Optimization in EEG Analysis

被引：0
|
作者：
Huang, Lu [1 ,2 ]
Wang, Hong [3 ]
机构：
[1] Northeastern Univ, Sinodutch Biomed & Informat Engn Sch, Shenyang 110819, Liaoning, Peoples R China
[2] Dalian Ocean Univ, Coll Informat Engn, Dalian 116023, Peoples R China
[3] Northeastern Univ, POB 319, Shenyang 110004, Liaoning, Peoples R China
来源：
PROCEEDINGS OF 2013 CHINESE INTELLIGENT AUTOMATION CONFERENCE: INTELLIGENT AUTOMATION | 2013年 / 254卷
基金：
中国国家自然科学基金;
关键词：
Brain-computer interface (BCI); Electroencephalogram (EEG); Swarm intelligence optimization (SIO); PI; INDEPENDENT COMPONENT ANALYSIS; ALGORITHMS;
D O I：
10.1007/978-3-642-38524-7_75
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In order to improve time efficiency of brain-computer interface (BCI), swarm intelligence optimization (SIO) based algorithms were used to analyze electroencephalogram (EEG). Artificial fish swarm algorithm (AFSA), a SIO algorithm, initiated several points in feasible domain of de-mixing matrix w, and parallel searched. These mechanism made the algorithm iterate fasterly. Ulteriorly, the introduction of chaos searching enhanced convergence precision. The experiments ran on real EEG data for ocular artifacts removal and P300 extraction respectively. PIs were all less than 0.08 and iteration numbers were superior than classic algorithm. These results show SIO based algorithms can effectively reduce computation time for EEG based BCI with the operation precision remaining unimpaired.
引用
收藏
页码：683 / 691
页数：9
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