Feature extraction and classification of imagined motor movement electroencephalogram signals. (1st January 2013)

• Record Type: Journal Article
• Authors: Upadhyay, Rahul; Kankar, Pavan Kumar; Padhy, Prabin Kumar; Gupta, Vijay Kumar
• Is Part Of: International journal of biomedical engineering and technology. Volume 13:Number 2(2013)
• Page Start: 133
• Page End: 146
• Publication Date: 2013-01-01
• Subjects: BCI -- brain–computer interface -- Butterworth filter -- EEG -- electroencephalogram -- MSPCA -- multiscale principal component analysis -- PSD -- power spectral density -- SVM -- support vector machine; Biomedical engineering -- Periodicals; 610.28
• Languages: English
• ISSNs: 1752-6418

Abstract

Brain–Computer Interface (BCI) establishes a communication channel between brain and external world. BCI can control numerous applications, such as controlling a cursor on computer screen, movement of a robotic arm or a wheel chair and many more. The efficiency and accuracy of BCI systems completely relies on efficient preprocessing and classification algorithms. In present work, the reliability of a BCI has been analysed, which is implemented using Electroencephalogram (EEG) signals, recorded from motor imagery, for imagination of three different mental tasks: left fist blink, right fist blink and both fists blink. The recorded EEG signals were primarily filtered out by a low pass Butterworth filter and further preprocessed using multiscale principal component analysis. Statistical parameters have been calculated from preprocessed EEG signals as features. For classification of the EEG signals, support vector machine model is employed.