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. 1. Introduction In recent years, brain computer interface and intelligent signal segmentation have attracted a great interest ranging from medicine to military objectives [1–6]. To facilitate brain-computer interface assembly, a professional method of feature extraction from EEG signal is desired. The brain electrical activity is represented by the electroencephalogram (EEG) signals. Many neurological diseases (i.e., epilepsy) can be diagnosed by studying the EEG signals [7–9]. The recoding of the EEG signals is performed by fixing an electrode on the subject scalp using the standardized electrode placement scheme (Figure 1) [10–12]. However, there are many sources of artifacts. The signal noise which can set in when signal is being captured will adversely affect the useful feature in the original signal. The major sources of the artifact are muscular activities, blinking of eyes during signal acquisition procedure, and power line electrical noise [13]. Many methods have been introduced to eliminate these unwanted signals. Each of them has its advantages and disadvantages. Nevertheless, there is a common path for EEG signal processing (Figure 2). The first part is preprocessing which includes acquisition of
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