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The non-stationary nature of the electroencephalogram (EEG) poses a major challenge for the successful operation of a brain-computer interface (BCI) when deployed over multiple sessions. The changes between the early training measurements and the proceeding multiple sessions can originate as a result of alterations in the subject's brain process, new cortical activities, change of recording conditions and/or change of operation strategies by the subject. These differences and alterations over multiple sessions cause deterioration in BCI system performance if periodic or continuous adaptation to the signal processing is not carried out. In this work, the covariate shift is analyzed over multiple sessions to determine the non-stationarity effects and an unsupervised adaptation approach is employed to account for the degrading effects this might have on performance. To improve the system's online performance, we propose a covariate shift minimization (CSM) method, which takes into account the distribution shift in the feature set domain to reduce the feature set overlap and unbalance for different classes. The analysis and the results demonstrate the importance of CSM, as this method not only improves the accuracy of the system, but also reduces the classification unbalance for different classes by a significant amount.