Motor Imagery Classification is a pivotal task, facilitating direct communication between the human brain and external devices. Traditional methodologies often rely on manual feature engineering and basic classifiers, posing limitations in capturing intricate patterns within brain signals. Addressing this challenge, we propose a pioneering transformer-based framework tailored to motor imagery classification in BCIs. Our model capitalizes on the inherent self-attention mechanism of transformers to autonomously discern hierarchical representations from EEG signals, thereby adeptly capturing both spatial and temporal dependencies. Through rigorous experimentation on publicly available EEG datasets such as the BCI Competition IV 2a dataset designed for motor imagery tasks, we showcase the efficacy of NeuroTransformer architecture with an accuracy of 86.2%, Sensitivity of 83.5% and Precision of 85.4%. Additionally, incorporating Principal Component Analysis with NeuroTransformer yields an accuracy of 86.7%, Sensitivity of 82.8%, and Precision of 86.1%. In the future, we focus on handling the problems associated with inter and intra-subject variability.