Constructing the Brain Rhythm Entropy Matrix (BREM) as a feature for emotion recognition is feasible due to the correlation between brain rhythm activities and arousal/valence levels, as well as the effectiveness of entropy in quantifying such activities. Besides, similarity, a vital metric in bioinformatics, measures the degree of resemblance between internal elements. Building on these foundations, this paper introduces an emotion recognition method that leverages the similarity measures of BREM derived from EEG signals. The results using the DEAP database indicate that the proposed method achieves an accuracy range of 75% to 92% by employing the optimal single-channel data for emotion recognition. Furthermore, significant individual differences were observed in selected channels and time windows for emotion recognition. Such findings not only offer an innovative manner for EEG-based emotion recognition through BREM and similarity measures but also provide advances for designing portable emotion computing devices in the future.