{"type":"ScholarlyArticle","title":"Category-aware EEG Image Generation Based on Wavelet Transform and Contrast Semantic Loss","authors":[{"name":"Enshang Zhang","slug":"enshang-zhang","given":"Enshang","family":"Zhang","affiliation":""},{"name":"Zhicheng Zhang","slug":"zhicheng-zhang","given":"Zhicheng","family":"Zhang","affiliation":""},{"name":"Takashi Hanakawa","slug":"takashi-hanakawa","given":"Takashi","family":"Hanakawa","affiliation":""}],"abstract":"Reconstructing visual stimuli from EEG signals is a crucial step in realizing brain-computer interfaces. In this paper, we propose a transformer-based EEG signal encoder integrating the Discrete Wavelet Transform (DWT) and the gating mechanism. Guided by the feature alignment and category-aware fusion losses, this encoder is used to extract features related to visual stimuli from EEG signals. Subsequently, with the aid of a pre-trained diffusion model, these features are reconstructed into visual stimuli. To verify the effectiveness of the model, we conducted EEG-to-image generation and classification tasks using the THINGS-EEG dataset. To address the limitations of quantitative analysis at the semantic level, we combined WordNet-based classification and semantic similarity metrics to propose a novel semantic-based score, emphasizing the ability of our model to transfer neural activities into visual representations. Experimental results show that our model significantly improves semantic alignment and classification accuracy, which achieves a maximum single-subject accuracy of 43%, outperforming other state-of-the-art methods. The source code is available at https://github.com/zes0v0inn/DWT_EEG_Reconstruction/.","doi":"10.24963/ijcai.2025/881","url":"https://ijcai.org/proceedings/2025/881","pdf":"https://ijcai.org/proceedings/2025/0881.pdf","markdown":"https://ijcai.org/proceedings/2025/881.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/881","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":881,"pages":{"first":7922,"last":7930},"keywords":["Computer Vision: CV: Image and video synthesis and generation","Machine Learning: ML: Attention models","Multidisciplinary Topics and Applications: MTA: Health and medicine","Machine Learning: ML: Supervised Learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}