{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:37:01Z","timestamp":1773801421847,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Visual neural decoding is an important research topic at the intersection of cognitive neuroscience and machine learning. While recent progress has been made in EEG-based neural decoding, reconstructing dynamic visual content remains challenging. In the field of EEG decoding, current models either utilize pre-trained encoders for feature extraction or employ graph neural networks to represent the spatio-temporal information embedding, resulting in poor model representation and high complexity. We propose EVOKE -- an innovative framework for zero-shot decoding of high-fidelity videos from EEG signals. EVOKE employs Implicit Neural Representations to perform complete spatial modeling of EEG and continuously decouples information in the EEG-INR perceptual space. Additionally, we construct a Hierarchical-aware Attention Module (HAM) to decode EEG from three feature anchors: visual, semantic, motion, and progressively control task inference. The Motion Attention Flow (MAF) we developed overcomes the limitations of capturing motion features in dynamic stimuli, creating a more robust representation that enhances reconstruction consistency. Comprehensive experiments prove that SOTA performance of EVOKE (0.353 SSIM, 0.715 CLIP-pcc). We provide an effective method for converting brain activity into rich visual experiences and set a new benchmark for brain multimodal generation.<\/jats:p>","DOI":"10.1609\/aaai.v40i7.37472","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:21:17Z","timestamp":1773789677000},"page":"5539-5547","source":"Crossref","is-referenced-by-count":0,"title":["EVOKE: Efficient and High-Fidelity EEG-to-Video Reconstruction via Decoupling Implicit Neural Representation"],"prefix":"10.1609","volume":"40","author":[{"given":"Haodong","family":"Jing","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Panqi","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongyao","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhipeng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nanning","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongqiang","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37472\/41434","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37472\/41434","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:21:18Z","timestamp":1773789678000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/37472"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i7.37472","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}