Accuracy in identifying the subjects’ intentions for moving their different limbs from EEG signals is regarded as an important factor in the studies related to BCI. In fact, the complexity of motor-imagination and low amount of signal-to-noise ratio for EEG signal makes this identification as a difficult task. In order to overcome these complexities, many techniques such as various feature extraction methods, learning algorithms, and classifier schemes have been developed in this regard. However, conducting more research is necessary for improvement. The present study aimed to use an ensemble learning approach to improve the performance of MI-BCI systems. Therefore, filter bank common spatial pattern (FBCSP), as a well-known feature extraction method, was used to produce separable features from EEG signals. Accordingly, error correcting output codes (ECOC) was applied on several learning algorithms to classify four classes of motor imagery tasks. The proposed ECOC ensemble technique was tested on the data set 2a from BCI competition IV. Based on the results, the ECOC can lead to an improvement by using the naive Bayesian parzen window algorithm, compared to the winner algorithm of BCI competition IV, which is superior to other selected state of the art algorithms.