The ability to feel, adapt, reason, remember and communicate makes human a social being. Disabilities limit opportunities and capabilities to socialize. With the recent advancement in brain-computer interface (BCI) technology, researchers are exploring if BCI can be augmented with human computer interaction (HCI) to give a new hope of restoring independence to disabled individuals. This motivates us to lay down our research objective, which is as follows. In this study, we propose to work with a hands-free text entry application based on the brain signals, for the task of communication, where the user can select a letter or word based on the intentions of left or right hand movement. The two major challenges that have been addressed are (i) interacting with only two imagery signals (ii) how a low-quality, noisy EEG signal can be competently processed and classified using novel combination of feature set to make the interface work efficiently. The results of five able-bodied users show that the error rate per minute is significantly reduced and it also illustrates that it can be further used to develop better BCI augmented HCI systems.