The accurate emotional assessment of humans can prove beneficial in health care, security investigations and human interaction. In contrast to emotion recognition from facial expressions which can prove to be inaccurate, analysis of electroencephalogram (EEG) activity is a more accurate representation of one’s state of mind. With advancements in deep learning, various methods are being employed for this task. In this research, importance of attention mechanism in EEG signals is introduced through two vision transformer based methods for the classification of EEG signals on the basis of emotions. The first method utilizes 2-D images generated through continuous wavelet transform (CWT) of the raw EEG signals and the second method directly operates on the raw signal. The publicly available and widely accepted DEAP dataset has been utilized in this research for validating the proposed approaches. The proposed approaches report very high accuracies of 97% and 95.75% using CWT and 99.4% and 99.1% using raw signal for valence and arousal classifications respectively, which clearly highlights the significance of attention mechanism for EEG signals. The proposed methodology also ensures faster training and testing time which suits the clinical purposes.Clinical Relevance— This work establishes a highly accurate algorithm for emotion recognition using EEG signals which has potential applications in music-based therapy.