SINGLE CHANNEL ELECTROENCEPHALOGRAM FEATURE EXTRACTION BASED ON PROBABILITY DENSITY FUNCTION FOR SYNCHRONOUS BRAIN COMPUTER INTERFACE

Over recent years, there has been an explosive growth of interest in Electroencephalogram (EEG) based-Brain Computer Interface (BCI). Technically any architecture of a BCI is designed to have the ability of extracting out a set of features from brain signal. This paper demonstrated the extraction process based on Probability Density Function (PDF).A shared control scheme was developed between a mobile robot and subject. In general, subjects were required to synchronously 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 is an object at certain direction. The mobile robot was then looking for a target based on probability value assigned to it. The result shows that 95% of theta activity was concentrated at target’s direction (during star imagination) and reduced when there is no target (during mind relaxation).

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Single channel electroencephalogram (EEG) brain computer interface (BCI) feature extraction and quantization method for support vector machine classification

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.12843 ◽

2018 ◽

Vol 7 (4) ◽

pp. 2095 ◽

Cited By ~ 1

Author(s):

Tarmizi Ahmad Izzuddin ◽

Norlaili Mat Safri ◽

Fauzal Naim Zohedi ◽

Mohamad Afzan Othman ◽

Muhammad Shaufil Adha Shawkany Hazim

Keyword(s):

Support Vector Machine ◽

Single Channel ◽

Brain Computer Interface ◽

Target Object ◽

International Standards ◽

Assistive Robotics ◽

Computer Interface ◽

Support Vector ◽

Electroencephalogram Eeg ◽

Bci System

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.

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