{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T22:19:36Z","timestamp":1769725176926,"version":"3.49.0"},"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":[[2020,7]]},"abstract":"<jats:p>Recent deep learning-based Brain-Computer Interface (BCI) decoding algorithms mainly focus on spatial-temporal features, while failing to explicitly explore spectral information which is one of the most important cues for BCI. In this paper, we propose a novel regional attention convolutional neural network (RACNN) to take full advantage of spectral-spatial-temporal features for EEG motion intention recognition. Time-frequency based analysis is adopted to reveal spectral-temporal features in terms of neural oscillations of primary sensorimotor. The basic idea of RACNN is to identify the activated area of the primary sensorimotor adaptively. The RACNN aggregates a varied number of spectral-temporal features produced by a backbone convolutional neural network into a compact fixed-length representation. Inspired by the neuroscience findings that functional asymmetry of the cerebral hemisphere, we propose a region biased loss to encourage high attention weights for the most critical regions. Extensive evaluations on two benchmark datasets and real-world BCI dataset show that our approach significantly outperforms previous methods.<\/jats:p>","DOI":"10.24963\/ijcai.2020\/218","type":"proceedings-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T12:12:10Z","timestamp":1594210330000},"page":"1570-1576","source":"Crossref","is-referenced-by-count":20,"title":["Learning Regional Attention Convolutional Neural Network for Motion Intention Recognition Based on EEG Data"],"prefix":"10.24963","author":[{"given":"Zhijie","family":"Fang","sequence":"first","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences"},{"name":"University of Chinese Academy of Sciences"}]},{"given":"Weiqun","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences"},{"name":"University of Chinese Academy of Sciences"}]},{"given":"Shixin","family":"Ren","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences"},{"name":"University of Chinese Academy of Sciences"}]},{"given":"Jiaxing","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences"},{"name":"University of Chinese Academy of Sciences"}]},{"given":"Weiguo","family":"Shi","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences"},{"name":"University of Chinese Academy of Sciences"}]},{"given":"Xu","family":"Liang","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences"},{"name":"University of Chinese Academy of Sciences"}]},{"given":"Chen-Chen","family":"Fan","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences"},{"name":"University of Chinese Academy of Sciences"}]},{"given":"Zeng-Guang","family":"Hou","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences"},{"name":"University of Chinese Academy of Sciences"},{"name":"Center for Excellence in Brain Science and Intelligence Technology"}]}],"member":"10584","event":{"name":"Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}","theme":"Artificial Intelligence","location":"Yokohama, Japan","acronym":"IJCAI-PRICAI-2020","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2020,7,11]]},"end":{"date-parts":[[2020,7,17]]}},"container-title":["Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T02:13:55Z","timestamp":1594260835000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2020\/218"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2020\/218","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}