{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T06:17:06Z","timestamp":1773901026917,"version":"3.50.1"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T00:00:00Z","timestamp":1619481600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T00:00:00Z","timestamp":1619481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Key-Area Research and Development Program of Guangdong Province, China","award":["2020B010165004"],"award-info":[{"award-number":["2020B010165004"]}]},{"name":"Shenzhen Science and Technology Program","award":["JCYJ20200109110208764"],"award-info":[{"award-number":["JCYJ20200109110208764"]}]},{"name":"Shenzhen Science and Technology Program","award":["JCYJ20200109110420626"],"award-info":[{"award-number":["JCYJ20200109110420626"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1813204"],"award-info":[{"award-number":["U1813204"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61802385"],"award-info":[{"award-number":["61802385"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072468"],"award-info":[{"award-number":["62072468"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong","doi-asserted-by":"crossref","award":["2021A1515012604"],"award-info":[{"award-number":["2021A1515012604"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"published-print":{"date-parts":[[2021,5]]},"DOI":"10.1007\/s11548-021-02377-2","type":"journal-article","created":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T21:37:32Z","timestamp":1619559452000},"page":"809-818","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Automatic identification of sweet spots from MERs for electrodes implantation in STN-DBS"],"prefix":"10.1007","volume":"16","author":[{"given":"Linxia","family":"Xiao","sequence":"first","affiliation":[]},{"given":"Caizi","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yanjiang","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3289-9714","authenticated-orcid":false,"given":"Weixin","family":"Si","sequence":"additional","affiliation":[]},{"given":"Doudou","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Hai","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Xiaodong","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Pheng-Ann","family":"Heng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,27]]},"reference":[{"issue":"1","key":"2377_CR1","doi-asserted-by":"publisher","first-page":"16013","DOI":"10.1088\/1741-2560\/13\/1\/016013","volume":"13","author":"SD Karamintziou","year":"2016","unstructured":"Karamintziou SD, Deligiannis NG, Piallat B, Polosan M, Chabard\u00e8s S, David O, Stathis PG, Tagaris GA, Boviatsis EJ, Sakas DE, Polychronaki GE, Tsirogiannis GL, Nikita KS (2016) Dominant efficiency of nonregular patterns of subthalamic nucleus deep brain stimulation for Parkinson\u2019s disease and obsessive-compulsive disorder in a data-driven computational model. J Neural Eng 13(1):16013","journal-title":"J Neural Eng"},{"issue":"S14","key":"2377_CR2","doi-asserted-by":"publisher","first-page":"S259","DOI":"10.1002\/mds.20960","volume":"21","author":"RE Gross","year":"2006","unstructured":"Gross RE, Krack P, Rodriguez-Oroz MC, Rezai AR, Benabid A (2006) Electrophysiological Mapping for the Implantation of Deep Brain Stimulators for Parkinson\u2019s Disease and Tremor. Movement Disord: official journal of the Movement Disorder Society 21(S14):S259\u2013S283","journal-title":"Movement Disord: official journal of the Movement Disorder Society"},{"key":"2377_CR3","doi-asserted-by":"crossref","unstructured":"Michmizos KP, Konstantina SN (2011) Addition of deep brain stimulation signal to a local field potential driven Izhikevich model masks the pathological firing pattern of an STN neuron. In:2011 annual international conference of the IEEE engineering in medicine and biology society, pp 7290\u20137293","DOI":"10.1109\/IEMBS.2011.6091700"},{"key":"2377_CR4","doi-asserted-by":"crossref","unstructured":"Novak P, Przybyszewski AW, Barborica A, Ravin p, Margolin L, Pilitsis JG, (2011) Localization of the subthalamic nucleus in Parkinson disease using multiunit activity. J Neurol Sci 310(1):44\u201349","DOI":"10.1016\/j.jns.2011.07.027"},{"key":"2377_CR5","unstructured":"Wang Z, Oates T (2015) Encoding time series as images for visual inspection and classification using tiled convolutional neural networks. In: twenty-ninth AAAI conference on artificial intelligence"},{"key":"2377_CR6","unstructured":"Hatami N, Gavet Y, Debayle J (2018) Classification of time-series images using deep convolutional neural networks. In: Tenth international conference on machine vision, pp 106960"},{"key":"2377_CR7","first-page":"1","volume":"8","author":"M Khosravi","year":"2020","unstructured":"Khosravi M, Atashzar SF, Gilmore G, Jog MS, Patel RV (2020) Intraoperative localization of STN during DBS surgery using a data-driven model. IEEE J Transl Eng He 8:1\u20139","journal-title":"IEEE J Transl Eng He"},{"issue":"1","key":"2377_CR8","doi-asserted-by":"publisher","first-page":"12050","DOI":"10.1088\/1742-6596\/705\/1\/012050","volume":"705","author":"L Schiaffino","year":"2016","unstructured":"Schiaffino L, Mu\u00f1oz A, Mart\u00ednez JG (2016) STN area detection using K-NN classifiers for MER recordings in Parkinson patients during neurostimulator implant surgery. J Phys Conf Series 705(1):12050","journal-title":"J Phys Conf Series"},{"key":"2377_CR9","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee JY, Kweon IS (2018) CBAM: convolutional block attention module. In:2018 International proceedings of the European conference on computer vision, pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"2377_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2019.08.018","volume":"363","author":"J Gao","year":"2019","unstructured":"Gao J, Wang Q, Yuan Y (2019) SCAR: spatial-\/channel-wise attention regression networks for crowd counting. Neurocomputing 363:1\u20138","journal-title":"Neurocomputing"},{"key":"2377_CR11","doi-asserted-by":"crossref","unstructured":"Wang Q, Wu B, Zhu P, Li P, Hu Q (2020) ECA-net: Efficient channel attention for deep convolutional neural networks. In: 2020 Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp.11534\u201311542","DOI":"10.1109\/CVPR42600.2020.01155"},{"issue":"1","key":"2377_CR12","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.clinph.2018.09.018","volume":"130","author":"KR Wan","year":"2019","unstructured":"Wan KR, Maszczyk T, See AAQ, Dauwels J, King NKK (2019) A review on microelectrode recording selection of features for machine learning in deep brain stimulation surgery for Parkinson\u2019s disease. Clin Neurophysiol 130(1):145\u2013154","journal-title":"Clin Neurophysiol"},{"issue":"5","key":"2377_CR13","doi-asserted-by":"publisher","first-page":"975","DOI":"10.1016\/j.clinph.2014.05.039","volume":"126","author":"V Rajpurohit","year":"2015","unstructured":"Rajpurohit V, Danish SF, Hargreaves EL, Wong S (2015) Optimizing computational feature sets for subthalamic nucleus localization in DBS surgery with feature selection. Clin Neurophysiol 126(5):975\u2013982","journal-title":"Clin Neurophysiol"},{"issue":"1","key":"2377_CR14","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1002\/mds.26806","volume":"32","author":"D Valsky","year":"2016","unstructured":"Valsky D, Marmor-Levin O, Deffains M, Eitan R, Blackwell KT, Bergman H, Israel Zvi (2016) Stop! border ahead: automatic detection of subthalamic exit during deep brain stimulation surgery. Mov Disord 32(1):70\u201379","journal-title":"Mov Disord"},{"key":"2377_CR15","doi-asserted-by":"crossref","unstructured":"Karthick PA, Wan KR, Qi A, Dauwels J, King N (2020) Automated detection of subthalamic nucleus in deep brain stimulation surgery for parkinson\u2019s disease using microelectrode recordings and wavelet packet features. J Neurosci Meth 343","DOI":"10.1016\/j.jneumeth.2020.108826"},{"issue":"2","key":"2377_CR16","doi-asserted-by":"publisher","first-page":"26006","DOI":"10.1088\/1741-2560\/6\/2\/026006","volume":"6","author":"S Wong","year":"2009","unstructured":"Wong S, Baltuch GH, Jaggi JL, Danish SF (2009) Functional localization and visualization of the subthalamic nucleus from microelectrode recordings acquired during DBS surgery with unsupervised machine learning. J Neural Eng 6(2):26006","journal-title":"J Neural Eng"},{"issue":"7","key":"2377_CR17","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1007\/s13042-017-0640-5","volume":"9","author":"H Cardona","year":"2018","unstructured":"Cardona H, Alvarez MA, Orozco AA (2018) Multi-task learning for subthalamic nucleus identification in deep brain stimulation. Int J Mach Learn Cyb 9(7):1181\u20131192","journal-title":"Int J Mach Learn Cyb"},{"key":"2377_CR18","doi-asserted-by":"crossref","unstructured":"Cao L, Li J, Zhou Y, Liu Y, Zhao Y, Liu H (2019) Online identification of functional regions in deep brain stimulation based on an unsupervised random forest with feature selection. J Nenural Eng 16(6)","DOI":"10.1088\/1741-2552\/ab2eb4"},{"key":"2377_CR19","unstructured":"Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: 3rd international conference on learning representations"},{"issue":"7","key":"2377_CR20","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/s10916-019-1345-y","volume":"43","author":"B Ay","year":"2019","unstructured":"Ay B, Yildirim O, Talo M, Baloglu UB, Aydin G, Puthankattil SD, Acharya UR (2019) Automated depression detection using deep representation and sequence learning with EEG signals. J Med Syst 43(7):205","journal-title":"J Med Syst"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-021-02377-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-021-02377-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-021-02377-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,20]],"date-time":"2021-05-20T08:59:26Z","timestamp":1621501166000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-021-02377-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,27]]},"references-count":20,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,5]]}},"alternative-id":["2377"],"URL":"https:\/\/doi.org\/10.1007\/s11548-021-02377-2","relation":{},"ISSN":["1861-6410","1861-6429"],"issn-type":[{"value":"1861-6410","type":"print"},{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,27]]},"assertion":[{"value":"10 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2021","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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}