Motor Imagery (MI) based brain-computer inter-faces have diverse applications from providing communication capability to paralyzed and locked-in patients to controlling movement of wheelchairs for patients with lack of motor control/functionality. Lack of adequate number of samples to train predictive models due to reliance on Single Trial EEG (STE) study designs in which a large portion of EEG data from a participant/patient is used for training a classifier that is only able to somewhat reliably predict thought patterns of that participant during the same BCI recording session is one of the main bottlenecks of BCI studies. Not having enough training samples limit the use of advance classification methods such as deep neural networks, methods that are known to have better prediction accuracy. Session Transfer, using previous sessions EEG data of the same subject, and Subject Transfer, using EEG data of other participants performing the same task, are found as mechanisms to address the limitations of STE-based BCI systems and increase their practicality. This paper proposes two subject transfer protocols representing “Zero Trial” and “Target SUbject Re-tuned”. A Convolutional Neural Network (CNN) is used to develop a predictive model capable of reliably stratifying MI patterns in target subject. The CNN performance is assessed using 3 datasets from BCI competitions with 2 and 3 MI classes. The results indicated feasibility of”Zero Trial” in the sense of providing above chance level predictions across all subjects. “Target Subject without re-tuning” subject transfer performed worse than both the “Zero Trial” and “Target Subject Re-tuned” albeit still above chance level. Yet, “Target Subject Re-tuned” subject transfer protocol outperformed STE and “Zero Trial” in most subjects.