{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T16:21:46Z","timestamp":1784737306874,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":19,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T00:00:00Z","timestamp":1639440000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Central government guidance for local science and technology development funds","award":["No.206Z0101G"],"award-info":[{"award-number":["No.206Z0101G"]}]},{"name":"Humanity and Social Science Foundation of the Ministry of Education of China","award":["21A13022003"],"award-info":[{"award-number":["21A13022003"]}]},{"name":"Zhejiang Provincial Social Science Fund","award":["20NDJC216YB"],"award-info":[{"award-number":["20NDJC216YB"]}]},{"name":"Zhejiang Provincial Educational Science Scheme 2021","award":["GH2021642"],"award-info":[{"award-number":["GH2021642"]}]},{"name":"National Natural Science Foundation of China Grant","award":["No. 72071049"],"award-info":[{"award-number":["No. 72071049"]}]},{"name":"The Hebei Natural Science Foundation","award":["No. F2020302001"],"award-info":[{"award-number":["No. F2020302001"]}]},{"name":"Hebei Academic of Science Technology Scheme","award":["No. 21604 and No. 21605"],"award-info":[{"award-number":["No. 21604 and No. 21605"]}]},{"name":"Zhejiang Provincial Natural Science Fund","award":["LY19F030010"],"award-info":[{"award-number":["LY19F030010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,14]]},"DOI":"10.1145\/3498851.3498955","type":"proceedings-article","created":{"date-parts":[[2022,4,11]],"date-time":"2022-04-11T22:34:41Z","timestamp":1649716481000},"page":"226-230","source":"Crossref","is-referenced-by-count":1,"title":["An enhanced ARIMA model for EEG classification"],"prefix":"10.1145","author":[{"given":"Yan","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computers and data, Ningbo Institute of Technology, Zhejiang University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihui","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computers and data, Ningbo Institute of Technology, Zhejiang University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baiying","family":"Xing","sequence":"additional","affiliation":[{"name":"School of Computers and data, Ningbo Institute of Technology, Zhejiang University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Computers and data, Ningbo Institute of Technology, Zhejiang University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunyu","family":"Feng","sequence":"additional","affiliation":[{"name":"Institute of mathematics, Hebei Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haolan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Center for SCDM, Ningbo Institute of technology, Zhejiang University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,4,11]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Research on EEG signal recognition based on machine learning [D]","author":"Yao Guo","year":"2019","unstructured":"Guo Yao . Research on EEG signal recognition based on machine learning [D] . Jinan University , 2019 Guo Yao. Research on EEG signal recognition based on machine learning [D]. Jinan University, 2019"},{"key":"e_1_3_2_1_2_1","volume-title":"Lin shirou. Classification method of motor imagery EEG signals based on deep convolution network [J]. Industrial control computer","author":"Zhao","year":"2021","unstructured":"Zhao longhui, Li Li , Chen Yihui , Lin shirou. Classification method of motor imagery EEG signals based on deep convolution network [J]. Industrial control computer , 2021 ,34 (06): 103-106 Zhao longhui, Li Li, Chen Yihui, Lin shirou. Classification method of motor imagery EEG signals based on deep convolution network [J]. Industrial control computer, 2021,34 (06): 103-106"},{"key":"e_1_3_2_1_3_1","volume-title":"Structural design and control of lower limb rehabilitation robot based on EEG signal [D]","author":"Qihui Zhu","year":"2020","unstructured":"Zhu Qihui . Structural design and control of lower limb rehabilitation robot based on EEG signal [D] . Nanjing University of Aeronautics and Astronautics , 2020 Zhu Qihui. Structural design and control of lower limb rehabilitation robot based on EEG signal [D]. Nanjing University of Aeronautics and Astronautics, 2020"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Zhou Xiaobo Zou Renling Huang Xiayang. Single upper limb functional movements decoding from motor imagery EEG signals using wavelet neural network[J]. Biomedical Signal Processing and Control 2021 70:  Zhou Xiaobo Zou Renling Huang Xiayang. Single upper limb functional movements decoding from motor imagery EEG signals using wavelet neural network[J]. Biomedical Signal Processing and Control 2021 70:","DOI":"10.1016\/j.bspc.2021.102965"},{"key":"e_1_3_2_1_5_1","first-page":"40","volume-title":"Engineering","author":"Eduardo","year":"2018","unstructured":"Eduardo nebot. Robotics \u2013 from automation to intelligent systems [J] . Engineering , 2018 ,4 (04): 40 - 46 Eduardo nebot. Robotics \u2013 from automation to intelligent systems [J]. Engineering, 2018,4 (04): 40-46"},{"key":"e_1_3_2_1_6_1","volume-title":"Research and evaluation of some processing methods of EEG signals [J]. Computer simulation","author":"Xie","year":"2007","unstructured":"Xie songyun, Zhang Zhenzhong , Yang Jinxiao , Research and evaluation of some processing methods of EEG signals [J]. Computer simulation , 2007 , 24 (2): 326-330 Xie songyun, Zhang Zhenzhong, Yang Jinxiao, Research and evaluation of some processing methods of EEG signals [J]. Computer simulation, 2007, 24 (2): 326-330"},{"key":"e_1_3_2_1_7_1","volume-title":"song Aiguo. EEG signal recognition method based on wavelet packet transform and cluster analysis [J]. Journal of instrumentation","author":"Baoguo Xu","year":"2009","unstructured":"Xu Baoguo , song Aiguo. EEG signal recognition method based on wavelet packet transform and cluster analysis [J]. Journal of instrumentation , 2009 , 030 (001): 25-28 Xu Baoguo, song Aiguo. EEG signal recognition method based on wavelet packet transform and cluster analysis [J]. Journal of instrumentation, 2009, 030 (001): 25-28"},{"key":"e_1_3_2_1_8_1","volume-title":"Improvement and application of classification algorithm based on C4.5 decision tree [J]. Computer technology and development","author":"Chunsheng Li","year":"2020","unstructured":"Li Chunsheng , Jiao Haitao , Liu Peng , Liu Xiaogang . Improvement and application of classification algorithm based on C4.5 decision tree [J]. Computer technology and development , 2020 ,30 (05): 185-189 Li Chunsheng, Jiao Haitao, Liu Peng, Liu Xiaogang. Improvement and application of classification algorithm based on C4.5 decision tree [J]. Computer technology and development, 2020,30 (05): 185-189"},{"key":"e_1_3_2_1_9_1","volume-title":"ye qiusun. Research and improvement of C4.5 algorithm in decision tree classification algorithm [J]. Computer system application","author":"Han","year":"2019","unstructured":"Han cunge , ye qiusun. Research and improvement of C4.5 algorithm in decision tree classification algorithm [J]. Computer system application , 2019 ,28 (06): 198-202 Han cunge, ye qiusun. Research and improvement of C4.5 algorithm in decision tree classification algorithm [J]. Computer system application, 2019,28 (06): 198-202"},{"issue":"2","key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1007\/s11045-018-0585-1","article-title":"RETRACTED ARTICLE: epileptic seizure detection by analyzing high dimensional phase space via Poincar\u00e9 section","volume":"30","author":"Selvakumari RS","year":"2019","unstructured":"Selvakumari RS , Mahalakshmi M ( 2019 ) RETRACTED ARTICLE: epileptic seizure detection by analyzing high dimensional phase space via Poincar\u00e9 section . Multidimens Syst Signal Process 30 ( 2 ): 1029 Selvakumari RS, Mahalakshmi M (2019) RETRACTED ARTICLE: epileptic seizure detection by analyzing high dimensional phase space via Poincar\u00e9 section. Multidimens Syst Signal Process 30(2):1029","journal-title":"Multidimens Syst Signal Process"},{"key":"e_1_3_2_1_11_1","volume-title":"sang Xin. Research and implementation of data mining algorithm based on C4.5 [J]. Science and technology innovation","author":"Haikun Pu","year":"2021","unstructured":"Pu Haikun , Gao Xin , sang Xin. Research and implementation of data mining algorithm based on C4.5 [J]. Science and technology innovation , 2021 (23): 55-56 Pu Haikun, Gao Xin, sang Xin. Research and implementation of data mining algorithm based on C4.5 [J]. Science and technology innovation, 2021 (23): 55-56"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2016.2585661"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","first-page":"230","DOI":"10.4172\/2161-0460.1000230","article-title":"Feature extraction of the Alzheimer's disease images using different optimization algorithms","volume":"6","author":"Dessouky MM","year":"2016","unstructured":"Dessouky MM , Elrashidy MA ( 2016 ) Feature extraction of the Alzheimer's disease images using different optimization algorithms . J Alzheimers Dis Parkinsonism 6 : 230 Dessouky MM, Elrashidy MA (2016) Feature extraction of the Alzheimer's disease images using different optimization algorithms. J Alzheimers Dis Parkinsonism 6:230","journal-title":"J Alzheimers Dis Parkinsonism"},{"key":"e_1_3_2_1_14_1","first-page":"584","volume-title":"Int Conf Electron Commun Aerosp Technol ICECA","author":"Rajaguru","year":"2017","unstructured":"Rajaguru H ( 2017 ) Sparse PCA and soft decision tree classifiers for epilepsy classification from EEG signals . Int Conf Electron Commun Aerosp Technol ICECA , pp. 581\u2013 584 Rajaguru H (2017) Sparse PCA and soft decision tree classifiers for epilepsy classification from EEG signals. Int Conf Electron Commun Aerosp Technol ICECA, pp. 581\u2013584"},{"key":"e_1_3_2_1_15_1","volume-title":"Lin shirou. Classification method of motor imagery EEG signals based on deep convolution network [J]. Industrial control computer","author":"Zhao","year":"2021","unstructured":"Zhao longhui, Li Li , Chen Yihui , Lin shirou. Classification method of motor imagery EEG signals based on deep convolution network [J]. Industrial control computer , 2021 ,34 (06): 103-106 Zhao longhui, Li Li, Chen Yihui, Lin shirou. Classification method of motor imagery EEG signals based on deep convolution network [J]. Industrial control computer, 2021,34 (06): 103-106"},{"key":"e_1_3_2_1_16_1","unstructured":"Qin Jing sun fali Hui Fang Wang Zumin Gao Bing Ji Changqing. Overview of key technologies and applications of wearable EEG devices [J \/ OL]. Computer applications: 1-7 [2021-10-24] http:\/\/kns.cnki.net\/kcms\/detail\/51.1307.TP.20210927.2045.034.html.  Qin Jing sun fali Hui Fang Wang Zumin Gao Bing Ji Changqing. Overview of key technologies and applications of wearable EEG devices [J \/ OL]. Computer applications: 1-7 [2021-10-24] http:\/\/kns.cnki.net\/kcms\/detail\/51.1307.TP.20210927.2045.034.html."},{"key":"e_1_3_2_1_17_1","volume-title":"An efficient scheme for mental task classification utilizing reflection coefficients obtained from autocorrelation function of EEG signal.\u00a0Brain Inf.\u00a05,\u00a01\u201312","author":"Chowdhury M.M.","year":"2018","unstructured":"Rahman, M.M. , Chowdhury , M.A. & Fattah , S. A. An efficient scheme for mental task classification utilizing reflection coefficients obtained from autocorrelation function of EEG signal.\u00a0Brain Inf.\u00a05,\u00a01\u201312 ( 2018 ). Rahman, M.M., Chowdhury, M.A. & Fattah, S.A. An efficient scheme for mental task classification utilizing reflection coefficients obtained from autocorrelation function of EEG signal.\u00a0Brain Inf.\u00a05,\u00a01\u201312 (2018)."},{"key":"e_1_3_2_1_18_1","first-page":"9","volume-title":"Baccal\u00e1 LA (eds)\u00a0Methods in brain connectivity inference through multivariate time series analysis","author":"Baccal\u00e1 LA","year":"2014","unstructured":"Baccal\u00e1 LA , Sameshima K ( 2014 ) Brain connectivity.\u00a0In: Sameshima K , Baccal\u00e1 LA (eds)\u00a0Methods in brain connectivity inference through multivariate time series analysis . CRC Press , Boca Raton , pp 1\u2013 9 Baccal\u00e1 LA, Sameshima K (2014) Brain connectivity.\u00a0In: Sameshima K, Baccal\u00e1 LA (eds)\u00a0Methods in brain connectivity inference through multivariate time series analysis. CRC Press, Boca Raton, pp 1\u20139"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00116251"}],"event":{"name":"WI-IAT '21: IEEE\/WIC\/ACM International Conference on Web Intelligence","location":"ESSENDON VIC Australia","acronym":"WI-IAT '21","sponsor":["SIGAI ACM Special Interest Group on Artificial Intelligence"]},"container-title":["IEEE\/WIC\/ACM International Conference on Web Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3498851.3498955","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3498851.3498955","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:49:09Z","timestamp":1750193349000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3498851.3498955"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,14]]},"references-count":19,"alternative-id":["10.1145\/3498851.3498955","10.1145\/3498851"],"URL":"https:\/\/doi.org\/10.1145\/3498851.3498955","relation":{},"subject":[],"published":{"date-parts":[[2021,12,14]]}}}