Classification of EEG Motor imagery multi class signals based on Cross Correlation

被引：24

作者：

Krishna, D. Hari ^{[1
]}

Pasha, I. A. ^{[1
]}

Savithri, T. Satya ^{[2
]}

机构：

[1] BV Raju Inst Technol, ECE Dept, Narsapur 502313, India

[2] JNTU Coll Engn, ECE Dept, Hyderabad 500085, Andhra Pradesh, India

来源：

INTERNATIONAL CONFERENCE ON COMPUTATIONAL MODELLING AND SECURITY (CMS 2016) | 2016年 / 85卷

关键词：

Brain Computer Interface (BCI); Motor Imagery; EEG multi class Classification; cross correlation; BRAIN-COMPUTER INTERFACES;

D O I：

10.1016/j.procs.2016.05.198

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Many techniques are developed for improving the classification performance of motor imagery (MI) signals used in Brain computer interfacing (BCI). Still there is scope for improvement of performance using various techniques. In this paper, cross correlation (CC) technique has been used for features extraction from EEG signal and the final classification was done based on voting method which selects the best classifier among the five classifiers used for classification. Our approach was tested on public data set 2a from BCI competition IV. The results proved that our approach outperformed already existing approaches with 29.82% improvement in kappa values.

引用

页码：490 / 495

页数：6

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