Electroencephalography (EEG) is a primary modality for estimating user intention in the brain-computer interface (BCI). In particular, BCI has been widely used to detect the intention of users in motor imagery (MI)-based tasks. Although the MI classification accuracy has been largely enhanced from previous efforts, MI-BCI studies have focused on extracting features only during MI tasks, not during the preparatory phases. The increment of alpha band power is induced by performing a task with attention. This study proposes a n approach for increasing MI-BCI performance by analyzing brain state in preparatory before the task. EEG recordings of nine healthy subjects from the open BCI dataset were investigated. The alpha lateralization index (ALI) was calculated for each trial and high ALI trials were utilized for learning lateralization-based model. MI classification accuracy using the lateralization-based model marked high performance (median accuracy = 63.2 %; interquartile range (IQR) = 50.0% - 54.8%) than the total trial-based approach (median accuracy = 52.0%; IQR = 50.0 % - 54.8%) with statistical significance (p = 0.018). This study suggests alpha lateralization which is an imbalance pattern between ipsilateral and contralateral is one of the main factors for improvement of performance. Accordingly, since the alpha liberalization before MI task could exert an effect on the MI phase, the analysis combined preparation with MI would derive highly benefit for the MI classification.