A method named brain rhythm sequencing has been proposed to interpret Electroencephalography (EEG) signal for emotion recognition. For this purpose, first, the reassigned smoothed pseudo Wigner-Ville distribution (RSPWVD) has been adopted. Then, all generated sequences have been evaluated for classifying the emotional states based on arousal and valence. Moreover, the classification was by similarity measurements between pairwise of the sequences. After evaluating the accuracies by using the sequences from different channels and time durations, on one hand, the significant channel for classification has been identified so that the recognition can be accomplished by considering single-channel. On the other hand, the particular duration which was valuable for classification has been found so that the size of required data can be reduced. Thus, emotion recognition can be performed by fewer data resource. Results revealed that the accuracy of ~70% has been realized and hence, the proposed method could open a new way to implement data analysis for EEG-based applications such as emotion recognition.