Brain-computer interfaces (BCIs) have been gaining momentum in making human-computer interaction more natural, especially for people with neuro-muscular disabilities. Among the existing solutions the systems relying on electroencephalograms (EEG) occupy the most prominent place due to their non-invasiveness. In this work we provide a review of various existing techniques for the identification of motor imagery (MI) tasks. More specifically we perform a comparison between CSP related features and features based on Power Spectral Density (PSD) techniques. Furthermore, for the identification of MI tasks two well-known classifiers are used, the Linear Discriminant Analysis (LDA) and the Support Vector Machine (SVM). Our results confirms that PSD features demonstrate the most consistent robustness and effectiveness in extracting patterns for accurately discriminating between left and right MI tasks.