{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T12:22:02Z","timestamp":1711455722283},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Core Research Grant of the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India","award":["CRG\/2021\/007147"]},{"name":"Core Research Grant of the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India","award":["CRG\/2021\/007147"]},{"DOI":"10.13039\/501100004541","name":"Ministry of Human Resource Development","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004541","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1007\/s11517-022-02692-z","type":"journal-article","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:02:31Z","timestamp":1665964951000},"page":"3567-3583","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["SNOAR: a new regression approach for the removal of ocular artifact from multi-channel electroencephalogram signals"],"prefix":"10.1007","volume":"60","author":[{"given":"Ruchi","family":"Juyal","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8929-3473","authenticated-orcid":false,"given":"Hariharan","family":"Muthusamy","sequence":"additional","affiliation":[]},{"given":"Niraj","family":"Kumar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"issue":"5","key":"2692_CR1","doi-asserted-by":"publisher","first-page":"987","DOI":"10.3390\/s19050987","volume":"19","author":"X Jiang","year":"2019","unstructured":"Jiang X, Bian G, Tian Z (2019) Removal of artifacts from EEG signals: a review. In Sensors 19(5):987. https:\/\/doi.org\/10.3390\/s19050987","journal-title":"In Sensors"},{"issue":"1","key":"2692_CR2","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/S0987-7053(00)00055-1","volume":"30","author":"RJ Croft","year":"2000","unstructured":"Croft RJ, Barry RJ (2000) Removal of ocular artifact from the EEG: a review. In Neurophysiologie Clinique 30(1):5\u201319. https:\/\/doi.org\/10.1016\/S0987-7053(00)00055-1","journal-title":"In Neurophysiologie Clinique"},{"issue":"2","key":"2692_CR3","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.ijpsycho.2004.03.007","volume":"53","author":"GL Wallstrom","year":"2004","unstructured":"Wallstrom GL, Kass RE, Miller A, Cohn JF, Fox NA (2004) Automatic correction of ocular artifacts in the EEG: a comparison of regression-based and component-based methods. International Journal of Psychophysiology 53(2):105\u2013119. https:\/\/doi.org\/10.1016\/j.ijpsycho.2004.03.007","journal-title":"International Journal of Psychophysiology"},{"key":"2692_CR4","doi-asserted-by":"publisher","first-page":"102741","DOI":"10.1016\/j.bspc.2021.102741","volume":"68","author":"W Mumtaz","year":"2021","unstructured":"Mumtaz W, Rasheed S, Irfan A (2021) Review of challenges associated with the EEG artifact removal methods. Biomed Signal Process Control 68:102741. https:\/\/doi.org\/10.1016\/j.bspc.2021.102741","journal-title":"Biomed Signal Process Control"},{"key":"2692_CR5","doi-asserted-by":"publisher","first-page":"1004","DOI":"10.1016\/j.dib.2016.06.032","volume":"8","author":"MA Klados","year":"2016","unstructured":"Klados MA, Bamidis PD (2016) A semi-simulated EEG\/EOG dataset for the comparison of EOG artifact rejection techniques. Data Brief 8:1004\u20131006. https:\/\/doi.org\/10.1016\/j.dib.2016.06.032","journal-title":"Data Brief"},{"key":"2692_CR6","doi-asserted-by":"publisher","unstructured":"Zhao S, Rudzicz F (2015) Classifying phonological categories in imagined and articulated speech. In: IEEE International Conference on Acoustics, Speech and Signal Processing, 922\u2013996. https:\/\/doi.org\/10.1109\/ICASSP.2015.7178118","DOI":"10.1109\/ICASSP.2015.7178118"},{"issue":"3","key":"2692_CR7","doi-asserted-by":"publisher","first-page":"960","DOI":"10.1016\/j.bbe.2021.06.007","volume":"41","author":"R Ranjan","year":"2021","unstructured":"Ranjan R, Sahana BC, Bhandari AK (2021) Ocular artifact elimination from electroencephalography signals: a systematic review. Biocybernetics and Biomedical Engineering 41(3):960\u2013996","journal-title":"Biocybernetics and Biomedical Engineering"},{"issue":"3","key":"2692_CR8","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1007\/BF02344717","volume":"42","author":"P He","year":"2004","unstructured":"He P, Wilson G, Russell C (2004) Removal of ocular artifacts from electro-encephalogram by adaptive filtering. Med Biol Eng Compu 42(3):407\u2013412. https:\/\/doi.org\/10.1007\/BF02344717","journal-title":"Med Biol Eng Compu"},{"issue":"3","key":"2692_CR9","doi-asserted-by":"publisher","first-page":"036007","DOI":"10.1088\/1741-2552\/aaac92","volume":"15","author":"B Somers","year":"2018","unstructured":"Somers B, Francart T, Bertrand A (2018) A generic EEG artifact removal algorithm based on the multi-channel Wiener filter. J Neural Eng 15(3):036007. https:\/\/doi.org\/10.1088\/1741-2552\/aaac92","journal-title":"J Neural Eng"},{"issue":"2","key":"2692_CR10","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.jneumeth.2006.05.033","volume":"158","author":"NP Castellanos","year":"2006","unstructured":"Castellanos NP, Makarov VA (2006) Recovering EEG brain signals: artifact suppression with wavelet enhanced independent component analysis. J Neurosci Methods 158(2):300\u2013312. https:\/\/doi.org\/10.1016\/j.jneumeth.2006.05.033","journal-title":"J Neurosci Methods"},{"key":"2692_CR11","doi-asserted-by":"publisher","unstructured":"Inuso G, La Foresta F, Mammone N, Morabito F C(2007) Wavelet-ICA methodology for efficient artifact removal from Electroencephalographic recordings. In: International Conference on Neural Networks - Conference Proceedings. IEEE, pp 1524\u20131529. https:\/\/doi.org\/10.1109\/IJCNN.2007.4371184","DOI":"10.1109\/IJCNN.2007.4371184"},{"key":"2692_CR12","doi-asserted-by":"publisher","unstructured":"Molla MKI, Tanaka T, Rutkowski TM (2012) Multivariate EMD based approach to EOG artifacts separation from EEG. In: International Conference on Acoustics, Speech and Signal Processing. IEEE, pp 653\u2013656. https:\/\/doi.org\/10.1109\/ICASSP.2012.6287968","DOI":"10.1109\/ICASSP.2012.6287968"},{"key":"2692_CR13","doi-asserted-by":"publisher","unstructured":"Molla Md KI, Tanaka T, Rutkowski TM, Cichocki A (2010) Separation of EOG artifacts from EEG signals using Bivariate EMD. In: International Conference on Acoustics, Speech and Signal Processing. IEEE, pp 562\u2013565. https:\/\/doi.org\/10.1109\/ICASS P.2010.5495594","DOI":"10.1109\/ICASS"},{"key":"2692_CR14","doi-asserted-by":"publisher","unstructured":"Damon C, Liutkus A, Gramfort A, Essid S (2013) Non-negative matrix factorization for single-channel EEG artifact rejection. In: International Conference on Acoustics Speech and Signal Processing. IEEE, pp 1177\u20131181. https:\/\/doi.org\/10.1109\/ICASSP. 2013. 6637836","DOI":"10.1109\/ICASSP"},{"issue":"5","key":"2692_CR15","doi-asserted-by":"publisher","first-page":"056012","DOI":"10.1088\/1741-2552\/abede0","volume":"18","author":"Y Gu","year":"2021","unstructured":"Gu Y, Li X, Chen S, Li X (2021) AOAR: an automatic ocular artifact removal approach for multi-channel electroencephalogram data based on non-negative matrix factorization and empirical mode decomposition. J Neural Eng 18(5):056012. https:\/\/doi.org\/10.1088\/1741-2552\/abede0","journal-title":"J Neural Eng"},{"issue":"23","key":"2692_CR16","doi-asserted-by":"publisher","first-page":"8279","DOI":"10.1109\/JSEN.2016.2560219","volume":"16","author":"AK Maddirala","year":"2016","unstructured":"Maddirala AK, Shaik RA (2016) Removal of EOG artifacts from single channel EEG signals using combined singular spectrum analysis and adaptive noise canceler. IEEE Sens J 16(23):8279\u20138287. https:\/\/doi.org\/10.1109\/JSEN.2016.2560219","journal-title":"IEEE Sens J"},{"key":"2692_CR17","doi-asserted-by":"publisher","first-page":"101987","DOI":"10.1016\/j.bspc.2020.101987","volume":"60","author":"SK Noorbasha","year":"2020","unstructured":"Noorbasha SK, Sudha GF (2020) Removal of EOG artifacts from single channel EEG \u2013 an efficient model combining overlap segmented ASSA and ANC. Biomed Signal Process Control 60:101987. https:\/\/doi.org\/10.1016\/j.bspc.2020.101987","journal-title":"Biomed Signal Process Control"},{"key":"2692_CR18","doi-asserted-by":"publisher","first-page":"102168","DOI":"10.1016\/j.bspc.2020.102168","volume":"63","author":"SK Noorbasha","year":"2021","unstructured":"Noorbasha SK, Sudha GF (2021) Removal of EOG artifacts and separation of different cerebral activity components from single channel EEG\u2014an efficient approach combining SSA\u2013ICA with wavelet thresholding for BCI applications. Biomed Signal Process Control 63:102168. https:\/\/doi.org\/10.1016\/j.bspc.2020.102168","journal-title":"Biomed Signal Process Control"},{"issue":"1","key":"2692_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-90437-7","volume":"11","author":"AK Maddirala","year":"2021","unstructured":"Maddirala AK, Veluvolu KC (2021) Eye-blink artifact removal from single channel EEG with k-means and SSA. Sci Rep 11(1):1\u20134","journal-title":"Sci Rep"},{"key":"2692_CR20","doi-asserted-by":"publisher","first-page":"101987","DOI":"10.1016\/j.compeleceng.2015.08.019","volume":"60","author":"R Patel","year":"2016","unstructured":"Patel R, Sengottuvel S, Janawadkar MP, Gireesan K, Radhakrishnan TS, Mariyappa N (2016) Ocular artifact suppression from EEG using ensemble empirical mode decomposition with principal component analysis. Comput Electr Eng 60:101987. https:\/\/doi.org\/10.1016\/j.compeleceng.2015.08.019","journal-title":"Comput Electr Eng"},{"key":"2692_CR21","doi-asserted-by":"publisher","unstructured":"Kanoga S, Mitsukura Y (2015) Eye blink artifact rejection in single-channel electroence-phalographic signals by complete ensemble empirical mode decomposition and independent component analysis. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, pp 121\u2013124. https:\/\/doi.org\/10.1109\/EMBC.2015.7318315","DOI":"10.1109\/EMBC.2015.7318315"},{"issue":"1","key":"2692_CR22","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1109\/TBME.2012.2225427","volume":"60","author":"KT Sweeney","year":"2013","unstructured":"Sweeney KT, McLoone SF, Ward TE (2013) The use of ensemble empirical mode decomposition with canonical correlation analysis as a novel artifact removal technique. IEEE Trans Biomed Eng 60(1):97\u2013105. https:\/\/doi.org\/10.1109\/TBME.2012.2225427","journal-title":"IEEE Trans Biomed Eng"},{"issue":"1","key":"2692_CR23","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1109\/JSEN.2019.2942153","volume":"20","author":"M Saini","year":"2020","unstructured":"Saini M, Payal SU (2020) An effective and robust framework for ocular artifact removal from single-channel EEG Signal based on variational mode decomposition. IEEE Sens J 20(1):369\u2013376","journal-title":"IEEE Sens J"},{"issue":"7","key":"2692_CR24","doi-asserted-by":"publisher","first-page":"3687","DOI":"10.1109\/JSEN.2019.2959697","volume":"20","author":"P Gajbhiye","year":"2020","unstructured":"Gajbhiye P, Tripathy RK, Pachori RB (2020) Elimination of ocular artifacts from single channel EEG signals using FBSE-EWT based rhythms. IEEE Sens J 20(7):3687\u20133696. https:\/\/doi.org\/10.1109\/JSEN.2019.2959697","journal-title":"IEEE Sens J"},{"issue":"3","key":"2692_CR25","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1007\/s11424-009-9171-9","volume":"22","author":"H Hassani","year":"2009","unstructured":"Hassani H, Zhigljavsky A (2009) Singular spectrum analysis: methodology and application to economics data. J Syst Sci Complexity 22(3):372\u2013394. https:\/\/doi.org\/10.1007\/s11424-009-9171-9","journal-title":"J Syst Sci Complexity"},{"key":"2692_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-48331-2","author":"GR Naik","year":"2015","unstructured":"Naik GR (2015) Non-negative matrix factorization techniques: advances in theory and applications. In Non-negative matrix factorization techniques: advances in theory and applications. https:\/\/doi.org\/10.1007\/978-3-662-48331-2","journal-title":"In Non-negative matrix factorization techniques: advances in theory and applications"},{"issue":"12","key":"2692_CR27","doi-asserted-by":"publisher","first-page":"355","DOI":"10.3390\/brainsci9120355","volume":"9","author":"MF Issa","year":"2019","unstructured":"Issa MF, Juhasz Z (2019) Improved EOG artifact removal using wavelet enhanced independent component analysis. Brain Sci 9(12):355. https:\/\/doi.org\/10.3390\/brainsci9120355","journal-title":"Brain Sci"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02692-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-022-02692-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02692-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T17:15:05Z","timestamp":1668014105000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-022-02692-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,17]]},"references-count":27,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["2692"],"URL":"http:\/\/dx.doi.org\/10.1007\/s11517-022-02692-z","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,17]]},"assertion":[{"value":"21 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 October 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}
