{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T06:58:47Z","timestamp":1774681127730,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>Decoding visual stimuli from brain activities is an interdisciplinary study of neuroscience and computer vision. With the emerging of Human-AI Collaboration, Human-Computer Interaction, and the development of advanced machine learning models, brain decoding based on deep learning attracts more attention. Electroencephalogram (EEG) is a widely used neurophysiology tool. Inspired by the success of deep learning on image representation and neural decoding, we proposed a visual-guided EEG decoding method that contains a decoding stage and a generation stage. In the classi\ufb01cation stage, we designed a visual-guided convolutional neural network (CNN) to obtain more discriminative representations from EEG, which are applied to achieve the classi\ufb01cation results. In the generation stage, the visual-guided EEG features are input to our improved deep generative model with a visual consistence module to generate corresponding visual stimuli. With the help of our visual-guided strategies, the proposed method outperforms traditional machine learning methods and deep learning models in the EEG decoding task.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/192","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"1387-1393","source":"Crossref","is-referenced-by-count":27,"title":["Decoding EEG by Visual-guided Deep Neural Networks"],"prefix":"10.24963","author":[{"given":"Zhicheng","family":"Jiao","sequence":"first","affiliation":[{"name":"Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA"}]},{"given":"Haoxuan","family":"You","sequence":"additional","affiliation":[{"name":"BNRist, KLISS, School of Software, Tsinghua University, China"}]},{"given":"Fan","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA"}]},{"given":"Xin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA"}]},{"given":"Han","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA"}]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:47:29Z","timestamp":1564300049000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/192"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/192","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}