The extraction of expressive features from an electroencephalography (EEG) signal is necessary for classification of movement and movement imagination of the limbs. We introduce different preprocessing and feature extraction algorithms for this purpose and develop an algorithm that selects features by their feature importance. This selection is used as an evaluation measure for features, their preprocessing algorithms and the EEG electrodes. Our results show that most influential features for signal interpretation are: common spatial patterns, fractal dimensions, as well as, variance and standard deviation of the preprocessed data. We show that preprocessing with continuous wavelet transforms outperforms the other tested preprocessing algorithms. Furthermore, we show that high gamma frequencies (70-90 Hz) contain more information than the lower μ-rhythms (8-12 Hz) where event-related-desynchronization (ERD) is known to occur. The important EEG electrodes for this classification task are located in the left and right back of the motor-cortex. The proposed algorithm can be further used to create subject-specific and performance models for real-time classification.