Table of Contents
ISRN Neuroscience
Volume 2014 (2014), Article ID 730218, 7 pages
Review Article

Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains

1Biomedical Systems and Informatics Engineering Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid 21163, Jordan
2Biomedical Engineering Department, Faculty of Engineering, Hashemite University, Zarqa 13115, Jordan

Received 20 October 2013; Accepted 9 January 2014; Published 13 February 2014

Academic Editors: A. Grant, J. A. Hinojosa, and M. S. Oliveira

Copyright © 2014 Amjed S. Al-Fahoum and Ausilah A. Al-Fraihat. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Technically, a feature represents a distinguishing property, a recognizable measurement, and a functional component obtained from a section of a pattern. Extracted features are meant to minimize the loss of important information embedded in the signal. In addition, they also simplify the amount of resources needed to describe a huge set of data accurately. This is necessary to minimize the complexity of implementation, to reduce the cost of information processing, and to cancel the potential need to compress the information. More recently, a variety of methods have been widely used to extract the features from EEG signals, among these methods are time frequency distributions (TFD), fast fourier transform (FFT), eigenvector methods (EM), wavelet transform (WT), and auto regressive method (ARM), and so on. In general, the analysis of EEG signal has been the subject of several studies, because of its ability to yield an objective mode of recording brain stimulation which is widely used in brain-computer interface researches with application in medical diagnosis and rehabilitation engineering. The purposes of this paper, therefore, shall be discussing some conventional methods of EEG feature extraction methods, comparing their performances for specific task, and finally, recommending the most suitable method for feature extraction based on performance.