Our study delves into the challenges of emotion recognition through electroencephalogram (EEG) signals in brain-computer interface systems. Recognizing the limitations of existing methods in accurately capturing intricate emotional patterns in EEG data, we propose a novel approach using asymmetric windowing recurrence plots (AWRP). This technique was designed to enhance the efficiency and accuracy of emotion recognition by encoding EEG signals into detailed image representations that are suitable for advanced deep neural network analysis.

Through empirical validations using benchmark datasets (DEAP and SEED), our method demonstrated significant improvements in classification accuracies, notably outperforming existing state-of-the-art methodologies. These findings not only contribute to the field of EEG-based emotion recognition, but also present a novel perspective that can guide future research in neural system analysis and rehabilitation engineering.Â