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<p>The capacity to interpret the brain’s processing of visual art via brain imaging techniques provides significant understanding of the cognitive mechanisms behind aesthetic appreciation. This study investigates these mechanisms through analyzing electroencephalography (EEG) data from participants performing two different tasks: gazing at a blank wall and viewing artworks. We created the ArtEEGAttention model, a novel deep learning architecture that employs sliding window convolution and multi-head self-attention mechanisms to accurately identify these varied viewing scenarios. Evaluated on a selected dataset of 16 individuals, with EEG signals separated into 3-second epochs and classified according to viewing environment, our model exhibited outstanding performance, with a remarkable cross-subject accuracy of 77.96%. The model’s remarkable accuracy, especially evident in specific subjects, highlights its robustness and superior generalization skills across various brain responses to art.</p>

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ArtEEGAttention: an advanced deep learning approach for art brain decoding

  • 期刊论文

  • 发表日期：

    2026年2月6日

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  • Shuming Hu,
  • Shu Zhang,
  • Ying Zhang,
  • Zhu Wang,
  • Bin Guo,
  • Zhiwen Yu

摘要

The capacity to interpret the brain’s processing of visual art via brain imaging techniques provides significant understanding of the cognitive mechanisms behind aesthetic appreciation. This study investigates these mechanisms through analyzing electroencephalography (EEG) data from participants performing two different tasks: gazing at a blank wall and viewing artworks. We created the ArtEEGAttention model, a novel deep learning architecture that employs sliding window convolution and multi-head self-attention mechanisms to accurately identify these varied viewing scenarios. Evaluated on a selected dataset of 16 individuals, with EEG signals separated into 3-second epochs and classified according to viewing environment, our model exhibited outstanding performance, with a remarkable cross-subject accuracy of 77.96%. The model’s remarkable accuracy, especially evident in specific subjects, highlights its robustness and superior generalization skills across various brain responses to art.