Artificial Bee Colony Based Feature Selection for Motor Imagery EEG Data

被引：16

作者：

Rakshit, Pratyusha ^{[1
]}

Bhattacharyya, Saugat ^{[2
]}

Konar, Amit ^{[1
]}

Khasnobish, Anwesha ^{[2
]}

Tibarewala, D. N. ^{[2
]}

Janarthanan, R. ^{[1
]}

机构：

[1] Jadavpur Univ, Dept Elect & Telecommun Engg, Kolkata 700032, India

[2] Jadavpur Univ, Sch Biosci & Engn, Kolkata 700032, India

来源：

PROCEEDINGS OF SEVENTH INTERNATIONAL CONFERENCE ON BIO-INSPIRED COMPUTING: THEORIES AND APPLICATIONS (BIC-TA 2012), VOL 2 | 2013年 / 202卷

关键词：

Brain-computer Interface; Electroencephalography; Motor Imagery; Feature Selection; Power Spectral Density; Artificial Bee Colony; COMPUTER INTERFACE TECHNOLOGY;

D O I：

10.1007/978-81-322-1041-2_11

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Brain-computer Interface (BCI) has widespread use in Neuro-rehabilitation engineering. Electroencephalograph (EEG) based BCI research aims to decode the various movement related data generated from the motor areas of the brain. One of the issues in BCI research is the presence of redundant data in the features of a given dataset, which not only increases the dimensions but also reduces the accuracy of the classifiers. In this paper, we aim to reduce the redundant features of a dataset to improve the accuracy of classification. For this, we have employed Artificial Bee Colony (ABC) cluster algorithm to reduce the features and have acquired their corresponding accuracy. It is seen that for a reduced features of 200, the highest accuracy of 64.29%. The results in this paper validate our claim.

引用

页码：127 / +

页数：3

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