For developing brain computer interface (BCI) applications, electroencephalography (EEG) is the most widely used measurement method due to its noninvasiveness, high temporal resolution, and portability. EEG signal contains sufficient neural information about each human task, which makes the extracting, and decoding of each task-related information is still challenging, especially to improve the existing BCI performances. In this paper, we present a comparison analysis to find the most relevant features and the most suitable classification method for decoding motor imagery for EEG-based BCI. Therefore, some signal processing and machine learning techniques have applied for features extraction and classification phases. For the decomposition of EEG signal, we used three type of features [EEG signal mean, root mean square (RMS) and Relative of band power (RBP)]. In addition, we investigated an analytical comparison between three methods of classification [Support Vector Machine (SVM), Linear Discriminant Analysis and K-Nearest Neighbors]. The methods were validated using a publicly available dataset (BCI Competition IV-III-a) to discriminate between two mental states (right and left hand movements) using 10-fold cross-validation. SVM method gave better classification accuracy of 76.4% using relative band powers as potential EEG features.