{"type":"ScholarlyArticle","title":"Learning Regional Attention Convolutional Neural Network for Motion Intention Recognition Based on EEG Data","authors":[{"name":"Zhijie Fang","slug":"zhijie-fang","given":"Zhijie","family":"Fang","affiliation":"The State Key Laboratory for Management and Control of Complex Systems , Institute of Automation , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences , Beijing , China"},{"name":"Weiqun Wang","slug":"weiqun-wang","given":"Weiqun","family":"Wang","affiliation":"The State Key Laboratory for Management and Control of Complex Systems , Institute of Automation , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences , Beijing , China"},{"name":"Shixin Ren","slug":"shixin-ren","given":"Shixin","family":"Ren","affiliation":"The State Key Laboratory for Management and Control of Complex Systems , Institute of Automation , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences , Beijing , China"},{"name":"Jiaxing Wang","slug":"jiaxing-wang","given":"Jiaxing","family":"Wang","affiliation":"The State Key Laboratory for Management and Control of Complex Systems , Institute of Automation , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences , Beijing , China"},{"name":"Weiguo Shi","slug":"weiguo-shi","given":"Weiguo","family":"Shi","affiliation":"The State Key Laboratory for Management and Control of Complex Systems , Institute of Automation , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences , Beijing , China"},{"name":"Xu Liang","slug":"xu-liang","given":"Xu","family":"Liang","affiliation":"The State Key Laboratory for Management and Control of Complex Systems , Institute of Automation , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences , Beijing , China"},{"name":"Chen-Chen Fan","slug":"chen-chen-fan","given":"Chen-Chen","family":"Fan","affiliation":"The State Key Laboratory for Management and Control of Complex Systems , Institute of Automation , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences , Beijing , China"},{"name":"Zeng-Guang Hou","slug":"zeng-guang-hou","given":"Zeng-Guang","family":"Hou","affiliation":"The State Key Laboratory for Management and Control of Complex Systems , Institute of Automation , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences , Beijing , China; Center for Excellence in Brain Science and Intelligence Technology , Beijing , China"}],"abstract":"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.","doi":"10.24963/ijcai.2020/218","url":"https://ijcai.org/proceedings/2020/218","pdf":"https://ijcai.org/proceedings/2020/0218.pdf","markdown":"https://ijcai.org/proceedings/2020/218.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/218","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Humans and AI","track":"Main track","number":218,"pages":{"first":1570,"last":1576},"keywords":["Humans and AI: Brain Sciences","Multidisciplinary Topics and Applications: AI for Life Science","Robotics: Human Robot Interaction"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24255/file_24255.m3u8","length_sec":286,"provider":"hls"}],"references":[{"position":1,"raw":"[Bashivan et al., 2016] Pouya Bashivan, Irina Rish, Mo- hammed Yeasin, and Noel Codella. Learning represen- tations from eeg with deep recurrent-convolutional neural networks. In International Conference on Learning Rep- resentations, (ICLR), 2016.","title":"Learning representations from eeg with deep recurrent-convolutional neural networks","authors":"Pouya Bashivan, Irina Rish, Mohammed Yeasin, Noel Codella","year":"2016","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Cecotti and Graser, 2010] Hubert Cecotti and Axel Graser. Convolutional neural networks for p300 detection with ap- plication to brain-computer interfaces. IEEE transactions on pattern analysis and machine intelligence, 33(3):433- 445, 2010.","title":"Convolutional neural networks for p300 detection with application to brain-computer interfaces","authors":"Graser Cecotti, Hubert Cecotti, Axel Graser","year":"2010","venue":"IEEE transactions on pattern analysis and machine intelligence","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2016] Duo Chen, Suiren Wan, and For- rest Sheng Bao. Epileptic focus localization using dis- crete wavelet transform based on interictal intracranial eeg. IEEE Transactions on Neural Systems and Rehabili- tation Engineering, 25(5):413-425, 2016.","title":"Epileptic focus localization using discrete wavelet transform based on interictal intracranial eeg","authors":"Chen","year":"2016","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen et al., 2018] Weitong Chen, Sen Wang, Xiang Zhang, Lina Yao, Lin Yue, Buyue Qian, and Xue Li. Eeg-based motion intention recognition via multi-task rnns. In Pro- ceedings of the 2018 SIAM International Conference on Data Mining, pages 279-287. SIAM, 2018.","title":"Eeg-based motion intention recognition via multi-task rnns","authors":"Weitong Chen, Sen Wang, Xiang Zhang, Lina Yao, Lin Yue, Buyue Qian, Xue Li","year":"2018","venue":"Proceedings of the 2018 SIAM International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cohen, 2014] Mike X Cohen. Analyzing neural time series data: theory and practice. MIT press, 2014.","title":"Analyzing neural time series data: theory and practice","authors":"Mike X Cohen","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Dornhege et al., 2003] Guido Dornhege, Benjamin Blankertz, Gabriel Curio, and Klaus-Robert Müller. Combining features for bci. In Advances in Neural Information Processing Systems, pages 1139-1146, 2003.","title":"Combining features for bci","authors":"Guido Dornhege, Benjamin Blankertz, Gabriel Curio, Klaus-Robert Müller","year":"2003","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Goldberger et al., 2000] Ary L Goldberger, Luis AN Ama- ral, Leon Glass, Jeffrey M Hausdorff, Plamen Ch Ivanov, Roger G Mark, Joseph E Mietus, George B Moody, Chung-Kang Peng, and H Eugene Stanley. Physiobank, physiotoolkit, and physionet: components of a new re- search resource for complex physiologic signals. Circu- lation, 101(23):e215-e220, 2000.","title":"Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals","authors":"Ary L Goldberger, A N Luis, Leon Amaral, Jeffrey M Glass, Plamen Hausdorff, Roger G Ch Ivanov, Joseph E Mark, George B Mietus, Chung-Kang Moody, H Eugene Peng, Stanley","year":"2000","venue":"Circulation","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Grosse-wentrup, 2009] Moritz Grosse-wentrup. Under- standing brain connectivity patterns during motor imagery for brain-computer interfacing. In D. Koller, D. Schuur- mans, Y. Bengio, and L. Bottou, editors, Advances in Neu- ral Information Processing Systems 21, pages 561-568.","title":"Understanding brain connectivity patterns during motor imagery for brain-computer interfacing","authors":"Moritz Grosse-Wentrup","year":null,"venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"Curran Associates, Inc., 2009.","title":null,"authors":"Curran Associates, Inc","year":"2009","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Kaneshiro et al., 2015] Blair Kaneshiro, Marcos Perreau Guimaraes, Hyung-Suk Kim, Anthony M Norcia, and Patrick Suppes. A representational similarity analysis of the dynamics of object processing using single-trial eeg classification. Plos one, 10(8):e0135697, 2015.","title":"A representational similarity analysis of the dynamics of object processing using single-trial eeg classification","authors":"Blair Kaneshiro, Marcos Perreau Guimaraes, Hyung-Suk Kim, Anthony M Norcia, Patrick Suppes","year":"2015","venue":"Plos one","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Lal et al., 2005] Thomas Navin Lal, Michael Schröder, N Jeremy Hill, Hubert Preissl, Thilo Hinterberger, Jürgen Mellinger, Martin Bogdan, Wolfgang Rosenstiel, Thomas Hofmann, Niels Birbaumer, et al. A brain computer inter- face with online feedback based on magnetoencephalogra- phy. In Proceedings of the 22nd international conference on Machine learning, pages 465-472. ACM, 2005.","title":"A brain computer interface with online feedback based on magnetoencephalography","authors":"Michael Thomas Navin Lal, Jeremy Schröder, Hubert Hill, Thilo Preissl, Jürgen Hinterberger, Martin Mellinger, Wolfgang Bogdan, Thomas Rosenstiel, Niels Hofmann, Birbaumer","year":"2005","venue":"Proceedings of the 22nd international conference on Machine learning","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Lawhern et al., 2018] Vernon J Lawhern, Amelia J Solon, Nicholas R Waytowich, Stephen M Gordon, Chou P Hung, and Brent J Lance. Eegnet: a compact convolutional neural network for eeg-based brain-computer interfaces. Journal of neural engineering, 15(5):056013, 2018.","title":"Eegnet: a compact convolutional neural network for eeg-based brain-computer interfaces","authors":"Amelia J Vernon J Lawhern, Nicholas R Solon, Waytowich, Stephen M Gordon, P Chou, Brent J Hung, Lance","year":"2018","venue":"Journal of neural engineering","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Ofner et al., 2017] Patrick Ofner, Andreas Schwarz, Joana Pereira, and Gernot R Müller-Putz. Upper limb move- ments can be decoded from the time-domain of low- frequency eeg. PloS one, 12(8):e0182578, 2017.","title":"Upper limb movements can be decoded from the time-domain of lowfrequency eeg","authors":"Patrick Ofner, Andreas Schwarz, Joana Pereira, Gernot R Müller-Putz","year":"2017","venue":"PloS one","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Park and Chung, 2019] Yongkoo Park and Wonzoo Chung. Selective feature generation method based on time domain parameters and correlation coefficients for filter-bank-csp bci systems. Sensors, 19(17):3769, 2019.","title":"Yongkoo Park and Wonzoo Chung. Selective feature generation method based on time domain parameters and correlation coefficients for filter-bank-csp bci systems","authors":"Chung Park","year":"2019","venue":"Sensors","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Sakhavi et al., 2018] Siavash Sakhavi, Cuntai Guan, and Shuicheng Yan. Learning temporal information for brain- computer interface using convolutional neural networks. IEEE transactions on neural networks and learning sys- tems, 29(11):5619-5629, 2018.","title":"Learning temporal information for braincomputer interface using convolutional neural networks","authors":"Siavash Sakhavi, Cuntai Guan, Shuicheng Yan","year":"2018","venue":"IEEE transactions on neural networks and learning systems","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Schalk et al., 2004] Gerwin Schalk, Dennis J McFarland, Thilo Hinterberger, Niels Birbaumer, and Jonathan R Wol- paw. Bci2000: a general-purpose brain-computer interface (bci) system. IEEE Transactions on biomedical engineer- ing, 51(6):1034-1043, 2004.","title":"Bci2000: a general-purpose brain-computer interface (bci) system","authors":"Gerwin Schalk, Dennis J Mcfarland, Thilo Hinterberger, Niels Birbaumer, Jonathan R Wolpaw","year":"2004","venue":"IEEE Transactions on biomedical engineering","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Schiatti et al., 2018] Lucia Schiatti, Jacopo Tessadori, Nikhil Deshpande, Giacinto Barresi, Louis C King, and Leonardo S Mattos. Human in the loop of robot learning: Eeg-based reward signal for target identification and reaching task. In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages 4473-4480. IEEE, 2018.","title":"Human in the loop of robot learning: Eeg-based reward signal for target identification and reaching task","authors":"Lucia Schiatti, Jacopo Tessadori, Nikhil Deshpande, Giacinto Barresi, Louis C King, Leonardo S Mattos","year":"2018","venue":"IEEE International Conference on Robotics and Automation (ICRA)","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Shan et al., 2018] Hongchang Shan, Yu Liu, and Todor P Stefanov. A simple convolutional neural network for ac- curate p300 detection and character spelling in brain com- puter interface. In IJCAI, pages 1604-1610, 2018.","title":"A simple convolutional neural network for accurate p300 detection and character spelling in brain computer interface","authors":"Hongchang Shan, Yu Liu, Todor P Stefanov","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/222"},{"position":19,"raw":"[Tang et al., 2016] Zhichuan Tang, Shouqian Sun, Sanyuan Zhang, Yumiao Chen, Chao Li, and Shi Chen. A brain- machine interface based on erd/ers for an upper-limb ex- oskeleton control. Sensors, 16(12):2050, 2016.","title":"A brainmachine interface based on erd/ers for an upper-limb exoskeleton control","authors":"Tang","year":"2016","venue":"Sensors","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Wu et al., 2014] Wei Wu, Zhe Chen, Xiaorong Gao, Yuan- qing Li, Emery N Brown, and Shangkai Gao. Probabilis- tic common spatial patterns for multichannel eeg analysis. IEEE transactions on pattern analysis and machine intel- ligence, 37(3):639-653, 2014.","title":"Probabilistic common spatial patterns for multichannel eeg analysis","authors":"Wei Wu, Zhe Chen, Xiaorong Gao, Yuanqing Li, Emery N Brown, Shangkai Gao","year":"2014","venue":"IEEE transactions on pattern analysis and machine intelligence","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Zhang et al., 2018] Dalin Zhang, Lina Yao, Xiang Zhang, Sen Wang, Weitong Chen, Robert Boots, and Boualem Be- natallah. Cascade and parallel convolutional recurrent neu- ral networks on eeg-based intention recognition for brain computer interface. In Thirty-Second AAAI Conference on Artificial Intelligence, 2018.","title":"Cascade and parallel convolutional recurrent neural networks on eeg-based intention recognition for brain computer interface","authors":"Dalin Zhang, Lina Yao, Xiang Zhang, Sen Wang, Weitong Chen, Robert Boots, Boualem Benatallah","year":"2018","venue":"Thirty-Second AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Zhang et al., 2019] Dalin Zhang, Lina Yao, Kaixuan Chen, Sen Wang, Pari Delir Haghighi, and Caley Sullivan. A graph-based hierarchical attention model for movement intention detection from eeg signals. IEEE Transac- tions on Neural Systems and Rehabilitation Engineering, 27(11):2247-2253, 2019.","title":"A graph-based hierarchical attention model for movement intention detection from eeg signals","authors":"Dalin Zhang, Lina Yao, Kaixuan Chen, Sen Wang, Pari Delir Haghighi, Caley Sullivan","year":"2019","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Zhao et al., 2019] Dongye Zhao, Fengzhen Tang, Bailu Si, and Xisheng Feng. Learning joint space-time-frequency features for eeg decoding on small labeled data. Neural Networks, 114:67-77, 2019.","title":"Learning joint space-time-frequency features for eeg decoding on small labeled data","authors":"Zhao","year":"2019","venue":"Neural Networks","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}