Brain-robot interface: Distinguishing left and right hand EEG signals through SVM

Author

Hajibabazadeh, Mahdiyeh ; Azimirad, Vahid

Author_Institution

Dept. of Mechatron. Eng., Univ. of Tabriz Tabriz, Tabriz, Iran

fYear

2014

fDate

15-17 Oct. 2014

Firstpage

813

Lastpage

816

Abstract

In this paper a new method of implementing brain-robot interface is presented. Motor imagery (MI) is kind of spontaneous EEG that is employed into the EEG-based BMIs. The features extraction and classification of EEG data related to the left and right hand motor imagery are performed. At first, the EEG signals from six channels are collected, and then filtered by low-pass filter. Wavelet transform decomposes the signal into frequency sub-bands as features. In the next step, support vector machine (SVM) classifies features in two classes: left or right hand motor imagery. The classification accuracy rate is 75%. Finally the output of classification is applied to move the arm of Tabriz-Puma robot.

Keywords

brain-computer interfaces; electroencephalography; human-robot interaction; low-pass filters; signal classification; support vector machines; wavelet transforms; EEG-based BMI; MI; SVM; Tabriz-Puma robot; brain-robot interface; features classification; features extraction; frequency sub-bands; hand EEG signals; low-pass filter; motor imagery; spontaneous EEG; support vector machine; wavelet transform; Classification algorithms; Electroencephalography; Feature extraction; Robots; Support vector machines; Wavelet transforms; EEG; Motor imagery; SVM; Wavelet transform;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Mechatronics (ICRoM), 2014 Second RSI/ISM International Conference on

Conference_Location

Tehran

Type

conf

DOI

10.1109/ICRoM.2014.6991004

Filename

6991004