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    Please use this identifier to cite or link to this item: http://ir.lib.ksu.edu.tw/handle/987654321/14340


    Title: Recognition of Imagery Tasks Using Principle Component Analysis and Neural Network Based on Particle Swarm Optimization
    Authors: Cheng-Jian Lin
    Ming-Hua Hsieh
    Chi-Yung Lee
    Keywords: Brain-Computer Interface
    neural networks
    particle swarm optimization
    migration
    principle component analysis
    Date: 2007-11-29
    Issue Date: 2008-01-02 12:48:57 (UTC+8)
    Abstract: The Brain-Computer Interface (BCI) is a system which transforms the brain activity of different mental tasks into a control signal. The system provides an augmentative communication method for patients with severe motor disabilities. In this paper, a neural classifier based on improved particle swarm optimization (IPSO) is proposed to classify an electroencephalogram (EEG) of mental tasks for left hand movement imagination, right hand movement imagination, and word generation. First, the EEG patterns utilize principle component analysis (PCA) in order to reduce the feature dimensions. Then a three-layer neural network trained using particle swarm optimization is used to realize a classifier. The proposed IPSO method consists of the modified evolutionary direction operator (MEDO) and the traditional PSO. The proposed MEDO combines the evolutionary direction operator (EDO) and the migration. The MEDO can strengthen the searching global solution. The IPSO algorithm can prevent premature convergence and outperform the other existing methods. Experimental results have shown that our method performs well for the classification of mental tasks from EEG data.
    Appears in Collections:[資訊科技學院 ] 2007年優質家庭生活科技(U-home)之關鍵技術研討會

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