Particle Swarm Optimization Neural Network based Classification of Mental Tasks

被引：0

作者：

Hema, C. R. ^{[1
]}

Paulraj, M. P. ^{[1
]}

Yaacob, S. ^{[1
]}

Adom, A. H. ^{[1
]}

Nagarajan, R. ^{[1
]}

机构：

[1] Univ Malaysia Perlis, Sch Mechatron Engn, Kangar, Perlis, Malaysia

来源：

4TH KUALA LUMPUR INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING 2008, VOLS 1 AND 2 | 2008年 / 21卷 / 1-2期

关键词：

EEG Signal Processing; Brain Machine Interfaces; PSO Neural Networks;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Classification of Mental Task EEG signals is a modus operandi for designing brain machine interfaces. Brain interfaces are designed to rehabilitate people with neural disorders to communicate or control devices. This paper proposes a novel algorithm to classify mental task signals using a particle swarm optimization training procedure for recurrent neural networks. The neural network is trained and tested with mental task signals acquired from two subjects. An average classification performance of 89.9% is observed.

引用

页码：883 / 888

页数：6

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