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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2025,6,30]]},"abstract":"<jats:p>Recent works on visual classification tasks have leveraged EEG signals to provide additional supervisory information, further improving the performance of the models on natural images. However, previous methods often force machine models to directly match EEG signals, which involves the transfer of modal-specific representations, leading to potentially distorted alignment of modal-shared representations. Moreover, focusing solely on aligning individual sample features neglects the alignment of relationships between samples, making it difficult to capture the potential relational reasoning capabilities in EEG signals. This relational reasoning ability is key to the human brain\u2019s outstanding performance in visual classification tasks. Similarly, for a machine model, the complex relationships between instances are more critical than individual instances. Inspired by this, our idea is to enhance machine visual classification capabilities by imparting human-like relational reasoning, encouraging machine models to focus on the relational structure within EEG signals. To this end, we propose a brain-machine relation alignment method that constructs a cognitive model and a visual model to process EEG signals and visual images, respectively. Instead of forcing the visual model to mimic the output of an individual EEG data sample represented by the cognitive model, we encourage it to learn the mutual relations of EEG data samples. By penalizing the difference in relational structures between EEG signals and visual images, we facilitate the transfer of relational knowledge. Experiments demonstrate that the proposed method significantly improves the classification performance of the visual model. 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