{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T21:39:47Z","timestamp":1771018787619,"version":"3.50.1"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2018,11,16]],"date-time":"2018-11-16T00:00:00Z","timestamp":1542326400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"yasar university","award":["Bap 020"],"award-info":[{"award-number":["Bap 020"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2019,4]]},"DOI":"10.1007\/s11760-018-1383-9","type":"journal-article","created":{"date-parts":[[2018,11,16]],"date-time":"2018-11-16T15:21:48Z","timestamp":1542381708000},"page":"567-573","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["EEG motor movement classification based on cross-correlation with effective channel"],"prefix":"10.1007","volume":"13","author":[{"given":"Mohand Lokman","family":"Al-dabag","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nalan","family":"Ozkurt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,11,16]]},"reference":[{"key":"1383_CR1","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1007\/s11760-016-1020-4","volume":"11","author":"A Santill\u00e1n-Guzm\u00e1n","year":"2017","unstructured":"Santill\u00e1n-Guzm\u00e1n, A., Heute, U., Stephani, U., Galka, A.: Comparison of different methods to suppress muscle artifacts in EEG signals. Signal Image Video Process 11, 761\u2013768 (2017)","journal-title":"Signal Image Video Process"},{"key":"1383_CR2","doi-asserted-by":"publisher","first-page":"1217","DOI":"10.1007\/s00521-010-0370-z","volume":"19","author":"J Gao","year":"2010","unstructured":"Gao, J., Lin, P., Yang, Y., Wang, P., Zheng, C.: Real-time removal of ocular artifacts from EEG based on independent component analysis and manifold learning. Neural Comput Appl 19, 1217\u20131226 (2010)","journal-title":"Neural Comput Appl"},{"key":"1383_CR3","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.cmpb.2017.05.009","volume":"146","author":"R Zarei","year":"2017","unstructured":"Zarei, R., He, J., Siuly, S., Zhang, Y.: A PCA aided cross-covariance scheme for discriminative feature extraction from EEG signals. Comput Methods Prog Biomed 146, 47\u201357 (2017)","journal-title":"Comput Methods Prog Biomed"},{"key":"1383_CR4","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s11517-013-1123-9","volume":"52","author":"S Bhattacharyya","year":"2013","unstructured":"Bhattacharyya, S., Sengupta, A., Chakraborti, T., Konar, A., Tibarewala, D.N.: Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata. Med Biol Eng Comput 52, 131\u2013139 (2013)","journal-title":"Med Biol Eng Comput"},{"key":"1383_CR5","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.measurement.2016.02.059","volume":"86","author":"S Siuly","year":"2016","unstructured":"Siuly, S., Wang, H., Zhang, Y.: Detection of motor imagery EEG signals employing Na\u00efve Bayes based learning process. Measurement 86, 148\u2013158 (2016)","journal-title":"Measurement"},{"key":"1383_CR6","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.bspc.2009.03.005","volume":"4","author":"NF Ince","year":"2009","unstructured":"Ince, N.F., Goksu, F., Tewfik, A.H., Arica, S.: Adapting subject specific motor imagery EEG patterns in space\u2013time\u2013frequency for a brain computer interface. Biomed Signal Process Control 4, 236\u2013246 (2009)","journal-title":"Biomed Signal Process Control"},{"key":"1383_CR7","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.jneumeth.2016.12.010","volume":"278","author":"M Miao","year":"2017","unstructured":"Miao, M., Zeng, H., Wang, A., Zhao, C., Liu, F.: Discriminative spatial-frequency-temporal feature extraction and classification of motor imagery EEG: an sparse regression. J Neurosci Methods 278, 13\u201324 (2017)","journal-title":"J Neurosci Methods"},{"key":"1383_CR8","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.bspc.2016.10.015","volume":"32","author":"H Mirvaziri","year":"2016","unstructured":"Mirvaziri, H., Mobarakeh, Z.S.: Improvement of EEG-based motor imagery classification using ringtopology-based particle swarm optimization. Biomed Signal Process Control 32, 69\u201375 (2016)","journal-title":"Biomed Signal Process Control"},{"issue":"10","key":"1383_CR9","doi-asserted-by":"publisher","first-page":"2313","DOI":"10.1109\/TBME.2017.2667579","volume":"64","author":"CM McCrimmon","year":"2017","unstructured":"McCrimmon, C.M., Fu, J.L., Wang, M., Lopes, L.S., Wang, P.T., Karimi-Bidhendi, A., Liu, C.Y., Heydari, P., Nenadic, Z.: Performance assessment of a custom, portable, and low-cost brain-computer interface platform. IEEE Trans Biomed Eng 64(10), 2313\u20132320 (2017)","journal-title":"IEEE Trans Biomed Eng"},{"key":"1383_CR10","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1109\/JAS.2016.7510121","volume":"4","author":"C Liu","year":"2017","unstructured":"Liu, C., Fu, Y., Yang, J., Xiong, X., Sun, H., Yu, Z.: Discrimination of motor imagery pattern by electroencephalogram phase synchronization combined with frequency band energy. IEEE J Autom Sinica 4, 551\u2013557 (2017)","journal-title":"IEEE J Autom Sinica"},{"key":"1383_CR11","doi-asserted-by":"crossref","unstructured":"Krishna, D.H., Pasha, I.A., Savithri, T.S.: Autonomuos robot control based on EEG and cross-correlation. In: International conference on intelligent systems and control, Coimbatore, pp 1\u20134 (2016)","DOI":"10.1109\/ISCO.2016.7727098"},{"key":"1383_CR12","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.neucom.2012.08.040","volume":"114","author":"A Ubeda","year":"2013","unstructured":"Ubeda, A., Ianez, E., Azor\u0131n, J.M., Sabater, J.M., Fernandez, E.: Classification method for BCIs based on the correlation of EEG maps. Neurocomputing 114, 98\u2013106 (2013)","journal-title":"Neurocomputing"},{"key":"1383_CR13","unstructured":"M\u00fcller KR, Blankertz B (2004) BCI competition dataset IVa. Intelligent Data Analysis Group and University Medicine Berlin. \n                    http:\/\/www.bbci.de\/competition\/iii\/desc_IVa.html\n                    \n                  . Accessed 24 Jan 2018"},{"key":"1383_CR14","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.bspc.2016.09.008","volume":"31","author":"M Li","year":"2017","unstructured":"Li, M., Chen, W., Zhang, T.: Classification of epilepsy EEG signals using DWT-based envelope analysis and neural network ensemble. Biomed Signal Process Control 31, 357\u2013365 (2017)","journal-title":"Biomed Signal Process Control"},{"key":"1383_CR15","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3264-1","volume-title":"The nature of statistical learning theory","author":"VN Vapnik","year":"2000","unstructured":"Vapnik, V.N.: The nature of statistical learning theory, 2nd edn. Springer, New York (2000)","edition":"2"},{"key":"1383_CR16","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1109\/86.895948","volume":"8","author":"E Haselsteiner","year":"2000","unstructured":"Haselsteiner, E., Pfurtscheller, G.: Using time-dependent neural networks for EEG classification. IEEE Trans Rehabil Eng 8, 457\u2013463 (2000)","journal-title":"IEEE Trans Rehabil Eng"},{"key":"1383_CR17","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/s11760-016-0943-0","volume":"11","author":"MN Tibdewal","year":"2017","unstructured":"Tibdewal, M.N., Fate, R.R., Mahadevappa, M., Ray, A.K.: Classification of artifactual EEG signal and detection of multiple eye movement artifact zones using novel Time-amplitude algorithm. Signal Image Video Process 11, 333\u2013340 (2017)","journal-title":"Signal Image Video Process"},{"key":"1383_CR18","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1109\/72.329697","volume":"5","author":"MT Hagan","year":"1994","unstructured":"Hagan, M.T., Menhaj, M.B.: Training feed-forward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5, 989\u2013993 (1994)","journal-title":"IEEE Trans Neural Netw"},{"key":"1383_CR19","volume-title":"Data mining concepts and techniques","author":"J Han","year":"2006","unstructured":"Han, J., Kamber, M.: Data mining concepts and techniques. Elsevier, San Francisco (2006)"},{"key":"1383_CR20","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1109\/TNSRE.2012.2184838","volume":"20","author":"S Siuly","year":"2012","unstructured":"Siuly, S., Li, Y.: Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain\u2013computer interface. IEEE Trans Neural Syst Rehabil Eng 20, 526\u2013538 (2012)","journal-title":"IEEE Trans Neural Syst Rehabil Eng"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-018-1383-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11760-018-1383-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-018-1383-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,11,16]],"date-time":"2019-11-16T00:28:22Z","timestamp":1573864102000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11760-018-1383-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,16]]},"references-count":20,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2019,4]]}},"alternative-id":["1383"],"URL":"https:\/\/doi.org\/10.1007\/s11760-018-1383-9","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,16]]},"assertion":[{"value":"25 January 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 October 2018","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 October 2018","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 November 2018","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}