Brain Computer Interfaces (BCI) have been emerged as an alternative to support automatic systems able to interpret brain functions, commonly, by analyzing electroencephalography (EEG) recordings. In this work, a time-series discrimination methodology, called Motor Imagery Discrimination by Relevance Analysis (MIDRA), is presented to support the development of BCI from EEG data. Particularly, a Motor Imagery (MI) paradigm is studied, i.e., imagination of left-right hand movements. In this sense, a feature relevance analysis strategy is presented to select representing characteristics using a variability criterion. Besides, short-time parameters are estimated from EEG data by considering both time and time-frequency representations to deal with non-stationary dynamics. MIDRA is assessed on two different BCI databases, a well-known MI data and an Emotiv-based dataset. Attained results showed that MIDRA enhances the BCI system performance in comparison with benchmark methods by suitable ranking the input feature set. Moreover, applying MIDRA in a BCI based on the Emotiv device is a straightforward alternative for dealing with MI paradigms.