Open AccessSingle channel electroencephalogram (EEG) brain computer interface (BCI) feature extraction and quantization method for support vector machine classification
Author(s) -
Tarmizi Ahmad Izzuddin,
Norlaili Mat Safri,
Fauzal Naim Zohedi,
Mohamed Othman,
Muhammad Hazim
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i4.12843
Subject(s) - brain–computer interface , support vector machine , computer science , electroencephalography , feature extraction , artificial intelligence , interface (matter) , motor imagery , teleoperation , channel (broadcasting) , pattern recognition (psychology) , computer vision , speech recognition , robot , psychology , computer network , bubble , psychiatry , maximum bubble pressure method , parallel computing
Over the recent years, there has been a huge interest towards Electroencephalogram (EEG) based brain computer interface (BCI) system. BCI system enables the extraction of meaningful information directly from the human brain via suitable signal processing and machine learning method and thus, many researches have applied this technology towards rehabilitation and assistive robotics. Such application is important towards improving the lives of people with motor diseases such as Amytrophic Lateral Scelorosis (ALS) disease or people with quadriplegia/tetraplegia. This paper introduces features extraction method based on the Fast Fourier Transform (FFT) with logarithmic bin-ning for rapid classification using Support Vector Machine (SVM) algorithm, with an application towards a BCI system with a shared con-trol scheme. In general, subjects wearing a single channel EEG electrode located at F8 (10-20 international standards) were required to syn-chronously imagine a star rotating and mind relaxation at specific time and direction. The imagination of a star would trigger a mobile robot suggesting that there exists a target object at certain direction. Based on the proposed algorithm, we showed that our algorithm can distin-guish between mind relaxation and mental star rotation with up to 80% accuracy from the single channel EEG signals.