{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T22:19:15Z","timestamp":1755037155989,"version":"3.41.0"},"reference-count":44,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2021,5,29]],"date-time":"2021-05-29T00:00:00Z","timestamp":1622246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61972384"],"award-info":[{"award-number":["61972384"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Outstanding Sino-foreign Youth Exchange Program of China Association for Science and Technology, and the Fundamental Research Funds for the Central Universities","award":["2412019FZ047"],"award-info":[{"award-number":["2412019FZ047"]}]},{"name":"RBWH IP-MAL Project","award":["46969"],"award-info":[{"award-number":["46969"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"crossref","award":["2017M621192"],"award-info":[{"award-number":["2017M621192"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"crossref"}]},{"name":"UQ, RBWH and UTS, and the ARC Project","award":["DE200101610"],"award-info":[{"award-number":["DE200101610"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2021,10,31]]},"abstract":"<jats:p>The brain\u2013computer interface (BCI) control technology that utilizes motor imagery to perform the desired action instead of manual operation will be widely used in smart environments. However, most of the research lacks robust feature representation of multi-channel EEG series, resulting in low intention recognition accuracy. This article proposes an EEG2Image based Denoised-ConvNets (called EID) to enhance feature representation of the intention recognition task. Specifically, we perform signal decomposition, slicing, and image mapping to decrease the noise from the irrelevant frequency bands. After that, we construct the Denoised-ConvNets structure to learn the colorspace and spatial variations of image objects without cropping new training images precisely. Toward further utilizing the color and spatial transformation layers, the colorspace and colored area of image objects have been enhanced and enlarged, respectively. In the multi-classification scenario, extensive experiments on publicly available EEG datasets confirm that the proposed method has better performance than state-of-the-art methods.<\/jats:p>","DOI":"10.1145\/3450449","type":"journal-article","created":{"date-parts":[[2021,5,30]],"date-time":"2021-05-30T01:59:11Z","timestamp":1622339951000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Exploring BCI Control in Smart Environments"],"prefix":"10.1145","volume":"15","author":[{"given":"Lin","family":"Yue","sequence":"first","affiliation":[{"name":"Northeast Normal University, Changchun, Jilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Shen","sequence":"additional","affiliation":[{"name":"Northeast Normal University, Changchun, Jilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sen","family":"Wang","sequence":"additional","affiliation":[{"name":"The University of Queensland, Brisbane, QLD, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"Boots","sequence":"additional","affiliation":[{"name":"Royal Brisbane Women\u2019s Hospital, Brisbane, QLD, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guodong","family":"Long","sequence":"additional","affiliation":[{"name":"The University of Technology Sydney, Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weitong","family":"Chen","sequence":"additional","affiliation":[{"name":"The University of Queensland, Brisbane, QLD, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaowei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Northeast Normal University, Changchun, Jilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,5,29]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_2_1_1_1","DOI":"10.1109\/APEMC.2015.7175377"},{"doi-asserted-by":"publisher","key":"e_1_2_1_2_1","DOI":"10.1016\/0167-8396(84)90029-3"},{"key":"e_1_2_1_3_1","first-page":"193","article-title":"EEG mouse: A machine learning-based brain computer interface","volume":"5","author":"Alomari Mohammad H.","year":"2014","unstructured":"Mohammad H. Alomari , Ayman AbuBaker , Aiman Turani , Ali M. Baniyounes , and Adnan Manasreh . 2014 . EEG mouse: A machine learning-based brain computer interface . Int. J. Adv. Comput. Sci. Appl 5 , 4 (2014), 193 \u2013 198 . Mohammad H. Alomari, Ayman AbuBaker, Aiman Turani, Ali M. Baniyounes, and Adnan Manasreh. 2014. EEG mouse: A machine learning-based brain computer interface. Int. J. Adv. Comput. Sci. Appl 5, 4 (2014), 193\u2013198.","journal-title":"Int. J. Adv. Comput. Sci. Appl"},{"unstructured":"Pouya Bashivan Irina Rish Mohammed Yeasin and Noel Codella. 2015. Learning representations from EEG with deep recurrent-convolutional neural networks. arXiv: https:\/\/scholar.google.com.hk\/scholar?cluster=4319242413507858773&hl=zh-CN&as_sdt=0 5.  Pouya Bashivan Irina Rish Mohammed Yeasin and Noel Codella. 2015. Learning representations from EEG with deep recurrent-convolutional neural networks. arXiv: https:\/\/scholar.google.com.hk\/scholar?cluster=4319242413507858773&hl=zh-CN&as_sdt=0 5.","key":"e_1_2_1_4_1"},{"key":"e_1_2_1_5_1","first-page":"19","article-title":"Arm motor function recovery during rehabilitation with the use of hand exoskeleton controlled by brain-computer interface: A patient with severe brain damage","volume":"42","author":"Biryukova E. V.","year":"2016","unstructured":"E. V. Biryukova , O. G. Pavlova , M. E. Kurganskaya , P. D. Bobrov , L. G. Turbina , A. A. Frolov , V. I. Davydov , A. V. Sil\u2019tchenko , and O. A. Mokienko . 2016 . Arm motor function recovery during rehabilitation with the use of hand exoskeleton controlled by brain-computer interface: A patient with severe brain damage . Fiziologiia Cheloveka 42 , 1 (2016), 19 \u2013 30 . E. V. Biryukova, O. G. Pavlova, M. E. Kurganskaya, P. D. Bobrov, L. G. Turbina, A. A. Frolov, V. I. Davydov, A. V. Sil\u2019tchenko, and O. A. Mokienko. 2016. Arm motor function recovery during rehabilitation with the use of hand exoskeleton controlled by brain-computer interface: A patient with severe brain damage. Fiziologiia Cheloveka 42, 1 (2016), 19\u201330.","journal-title":"Fiziologiia Cheloveka"},{"doi-asserted-by":"publisher","key":"e_1_2_1_6_1","DOI":"10.1109\/TNSRE.2020.3023417"},{"doi-asserted-by":"publisher","key":"e_1_2_1_7_1","DOI":"10.1137\/1.9781611975321.32"},{"key":"e_1_2_1_8_1","volume-title":"Sheng","author":"Chen Weitong","year":"2019","unstructured":"Weitong Chen , Lin Yue , Bohan Li , Can Wang , and Quan Z . Sheng . 2019 . DAMTRNN : A delta attention-based multi-task RNN for intention recognition. In International Conference on Advanced Data Mining and Applications. Springer , 373\u2013388. Weitong Chen, Lin Yue, Bohan Li, Can Wang, and Quan Z. Sheng. 2019. DAMTRNN: A delta attention-based multi-task RNN for intention recognition. In International Conference on Advanced Data Mining and Applications. Springer, 373\u2013388."},{"doi-asserted-by":"publisher","key":"e_1_2_1_9_1","DOI":"10.1145\/3371153"},{"doi-asserted-by":"publisher","key":"e_1_2_1_10_1","DOI":"10.23919\/MEASUREMENT.2017.7983581"},{"key":"e_1_2_1_11_1","first-page":"134","article-title":"EEG based brain controlled wheelchair for physically challenged people","volume":"4","author":"Folane N.","year":"2016","unstructured":"N. Folane and R. Autee . 2016 . EEG based brain controlled wheelchair for physically challenged people . International Journal of Innovative Research in Computer and Communication Engineering 4 , 1 (2016), 134 \u2013 137 . N. Folane and R. Autee. 2016. EEG based brain controlled wheelchair for physically challenged people. International Journal of Innovative Research in Computer and Communication Engineering 4, 1 (2016), 134\u2013137.","journal-title":"International Journal of Innovative Research in Computer and Communication Engineering"},{"doi-asserted-by":"publisher","key":"e_1_2_1_12_1","DOI":"10.14311\/NNW.2017.27.006"},{"doi-asserted-by":"publisher","key":"e_1_2_1_13_1","DOI":"10.1109\/TPAMI.2015.2389824"},{"unstructured":"Jaderberg Max and Simonyan Karen and Kavukcuoglu and Andrew Zisserman Koray. 2015. Spatial transformer networks. In Advances in Neural Information Processing Systems. 2017\u20132025.  Jaderberg Max and Simonyan Karen and Kavukcuoglu and Andrew Zisserman Koray. 2015. Spatial transformer networks. In Advances in Neural Information Processing Systems. 2017\u20132025.","key":"e_1_2_1_14_1"},{"doi-asserted-by":"publisher","key":"e_1_2_1_15_1","DOI":"10.1145\/3309541"},{"doi-asserted-by":"publisher","key":"e_1_2_1_16_1","DOI":"10.1109\/JBHI.2018.2832538"},{"doi-asserted-by":"publisher","key":"e_1_2_1_17_1","DOI":"10.1109\/TNSRE.2020.3040984"},{"doi-asserted-by":"publisher","key":"e_1_2_1_18_1","DOI":"10.1109\/TNNLS.2020.3015505"},{"key":"e_1_2_1_19_1","volume-title":"Clive C. Took, Danilo P. Mandic, and Cheolsoo Park.","author":"Kim Youngjoo","year":"2016","unstructured":"Youngjoo Kim , Jiwoo Ryu , Ko Keun Kim , Clive C. Took, Danilo P. Mandic, and Cheolsoo Park. 2016 . Motor imagery classification using mu and beta rhythms of EEG with strong uncorrelating transform based complex common spatial patterns. Computational Intelligence and Neuroscience 2016, Article 1489692 (2016), 1--11. Youngjoo Kim, Jiwoo Ryu, Ko Keun Kim, Clive C. Took, Danilo P. Mandic, and Cheolsoo Park. 2016. Motor imagery classification using mu and beta rhythms of EEG with strong uncorrelating transform based complex common spatial patterns. Computational Intelligence and Neuroscience 2016, Article 1489692 (2016), 1--11."},{"doi-asserted-by":"publisher","key":"e_1_2_1_20_1","DOI":"10.1007\/978-3-030-35231-8"},{"doi-asserted-by":"publisher","key":"e_1_2_1_21_1","DOI":"10.1016\/j.cmpb.2010.11.014"},{"doi-asserted-by":"publisher","key":"e_1_2_1_22_1","DOI":"10.1088\/1741-2552\/aab2f2"},{"key":"e_1_2_1_23_1","volume-title":"Conrad","author":"Major Tyler C.","year":"2017","unstructured":"Tyler C. Major and James M . Conrad . 2017 . The effects of pre-filtering and individualizing components for electroencephalography neural network classification. In SoutheastCon 2017. IEEE , 1\u20136. Tyler C. Major and James M. Conrad. 2017. The effects of pre-filtering and individualizing components for electroencephalography neural network classification. In SoutheastCon 2017. IEEE, 1\u20136."},{"doi-asserted-by":"crossref","unstructured":"D. Mishkin N. Sergievskiy and J. Matas. 2016. Systematic evaluation of CNN advances on the ImageNet. [J]. Computer Vision and Image Understanding (2017) 161: 11--19.  D. Mishkin N. Sergievskiy and J. Matas. 2016. Systematic evaluation of CNN advances on the ImageNet. [J]. Computer Vision and Image Understanding (2017) 161: 11--19.","key":"e_1_2_1_24_1","DOI":"10.1016\/j.cviu.2017.05.007"},{"doi-asserted-by":"publisher","key":"e_1_2_1_25_1","DOI":"10.3758\/s13415-016-0476-6"},{"doi-asserted-by":"publisher","key":"e_1_2_1_26_1","DOI":"10.1109\/CEEC.2014.6958559"},{"volume-title":"2016 1st International Conference on Technology and Innovation in Sports, Health and Wellbeing (TISHW\u201916)","author":"Pinheiro Oberdan R.","unstructured":"Oberdan R. Pinheiro , Lynn R. G. Alves , M. F. M. Romero , and Josemar R . de Souza. 2016. Wheelchair simulator game for training people with severe disabilities . In 2016 1st International Conference on Technology and Innovation in Sports, Health and Wellbeing (TISHW\u201916) . IEEE, 1\u20138. Oberdan R. Pinheiro, Lynn R. G. Alves, M. F. M. Romero, and Josemar R. de Souza. 2016. Wheelchair simulator game for training people with severe disabilities. In 2016 1st International Conference on Technology and Innovation in Sports, Health and Wellbeing (TISHW\u201916). IEEE, 1\u20138.","key":"e_1_2_1_27_1"},{"doi-asserted-by":"publisher","key":"e_1_2_1_28_1","DOI":"10.1109\/CEEICT.2016.7873081"},{"doi-asserted-by":"publisher","key":"e_1_2_1_29_1","DOI":"10.1088\/1741-2560\/2\/4\/L02"},{"doi-asserted-by":"publisher","key":"e_1_2_1_30_1","DOI":"10.1109\/ICICS.2015.7459836"},{"volume-title":"2013 International Conference on Control Communication and Computing (ICCC\u201913)","author":"Sita J.","unstructured":"J. Sita and G.J. Nair . 2013. Feature extraction and classification of eeg signals for mapping motor area of the brain . In 2013 International Conference on Control Communication and Computing (ICCC\u201913) . IEEE, 463\u2013468. J. Sita and G.J. Nair. 2013. Feature extraction and classification of eeg signals for mapping motor area of the brain. In 2013 International Conference on Control Communication and Computing (ICCC\u201913). IEEE, 463\u2013468.","key":"e_1_2_1_31_1"},{"doi-asserted-by":"publisher","key":"e_1_2_1_32_1","DOI":"10.1007\/978-3-319-21969-1_17"},{"doi-asserted-by":"publisher","key":"e_1_2_1_33_1","DOI":"10.1088\/1741-2560\/14\/1\/016003"},{"key":"e_1_2_1_34_1","first-page":"3","article-title":"Ellen r. grass lecture: Extraordinary eeg","volume":"54","author":"Tatum William O.","year":"2014","unstructured":"William O. Tatum . 2014 . Ellen r. grass lecture: Extraordinary eeg . The Neurodiagnostic Journal 54 , 1 (2014), 3 \u2013 21 . William O. Tatum. 2014. Ellen r. grass lecture: Extraordinary eeg. The Neurodiagnostic Journal 54, 1 (2014), 3\u201321.","journal-title":"The Neurodiagnostic Journal"},{"doi-asserted-by":"publisher","key":"e_1_2_1_35_1","DOI":"10.3389\/neuro.01.015.2009"},{"doi-asserted-by":"publisher","key":"e_1_2_1_36_1","DOI":"10.1109\/TKDE.2016.2605687"},{"doi-asserted-by":"publisher","key":"e_1_2_1_37_1","DOI":"10.1038\/s41587-019-0397-3"},{"key":"e_1_2_1_38_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"32","author":"Zhang Dalin","year":"2017","unstructured":"Dalin Zhang , Lina Yao , Xiang Zhang , Sen Wang , Weitong Chen , and Robert Boots . 2017 . Eeg-based intention recognition from spatio-temporal representations via cascade and parallel convolutional recurrent neural networks . In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 32 , No. 1. Dalin Zhang, Lina Yao, Xiang Zhang, Sen Wang, Weitong Chen, and Robert Boots. 2017. Eeg-based intention recognition from spatio-temporal representations via cascade and parallel convolutional recurrent neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 32, No. 1."},{"doi-asserted-by":"publisher","key":"e_1_2_1_39_1","DOI":"10.1371\/journal.pone.0074433"},{"doi-asserted-by":"publisher","key":"e_1_2_1_40_1","DOI":"10.1007\/978-3-319-70096-0_76"},{"doi-asserted-by":"publisher","key":"e_1_2_1_41_1","DOI":"10.1109\/PERCOM.2018.8444575"},{"key":"e_1_2_1_42_1","volume-title":"A survey on deep learning based brain computer interface: Recent advances and new frontiers. arXiv preprint arXiv:1905.04149","author":"Zhang Xiang","year":"2019","unstructured":"Xiang Zhang , Lina Yao , Xianzhi Wang , Jessica Monaghan , David Mcalpine , and Yu Zhang . 2019. A survey on deep learning based brain computer interface: Recent advances and new frontiers. arXiv preprint arXiv:1905.04149 ( 2019 ). Xiang Zhang, Lina Yao, Xianzhi Wang, Jessica Monaghan, David Mcalpine, and Yu Zhang. 2019. A survey on deep learning based brain computer interface: Recent advances and new frontiers. arXiv preprint arXiv:1905.04149 (2019)."},{"doi-asserted-by":"publisher","key":"e_1_2_1_43_1","DOI":"10.1016\/j.patcog.2012.07.018"},{"doi-asserted-by":"publisher","key":"e_1_2_1_44_1","DOI":"10.1109\/TIP.2014.2332764"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3450449","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3450449","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:45Z","timestamp":1750191525000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3450449"}},"subtitle":["Intention Recognition Via EEG Representation Enhancement Learning"],"short-title":[],"issued":{"date-parts":[[2021,5,29]]},"references-count":44,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,10,31]]}},"alternative-id":["10.1145\/3450449"],"URL":"https:\/\/doi.org\/10.1145\/3450449","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"type":"print","value":"1556-4681"},{"type":"electronic","value":"1556-472X"}],"subject":[],"published":{"date-parts":[[2021,5,29]]},"assertion":[{"value":"2020-04-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-01-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-05-29","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}