[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120472-en":3,"doc-seo-120472-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120472,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","ML-STIM - Machine Learning for SubThalamic nucleus Intraoperative Mapping","The document presents ML-STIM, a machine learning pipeline for intraoperative identification of the subthalamic nucleus (STN) from microelectrode recordings (MERs) used in deep brain stimulation for medication-refractory Parkinson’s disease. It targets limitations of operator-dependent MER analysis by proposing automated preprocessing, adaptive artifact removal, literature-informed feature extraction, and multi-layer perceptron classification. The method is trained and validated on a public dataset (46 patients) and tested for generalizability on an independent dataset from a different surgical center (36 patients), with Dataset B also released publicly.","POLITECNICO DI TORINO Repository ISTITUZIONALE  \nML-STIM: Machine learning for subthalamic nucleus intraoperative mapping  \nOriginal  \nML-STIM: Machine learning for subthalamic nucleus intraoperative mapping / Sciscenti, Fabrizio; Agostini, Valentina; Rizzi, Laura; Lanotte, Michele; Ghislieri, Marco. -In: JOURNAL OF NEURAL ENGINEERING. -ISSN 1741-2552. -ELETTRONICO. - (2025) . [10 . 1088/1741-2552/adf579]  \nAvailability:  \nThis version is available at: 11583/3002236 since: 2025-07-30T08:57:01Z  \nPublisher: IOP Science  \nPublished  \nDOI:10.1088/1741-2552/adf579  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \nIOP postprint/Author's Accepted Manuscript  \n“This is the accepted manuscript version of an article accepted for publication in JOURNAL OF NEURAL ENGINEERING. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at [http://dx.doi.org/10.1088/1741-2552/adf579](http://dx.doi.org/10.1088/1741-2552/adf579)  \n(Article begins on next page)  \n04 October 2025  \nJournal of Neural Engineering  \nACCEPTED MANUSCRIPT • OPEN ACCESS  \nML-STIM: Machine learning for subthalamic nucleus intraoperative mapping  \nTo cite this article before publication: [Fabrizio Sciscenti](Fabrizio Sciscenti et al 2025 J. Neural Eng. in)[ et al](Fabrizio Sciscenti et al 2025 J. Neural Eng. in)[ 2025](Fabrizio Sciscenti et al 2025 J. Neural Eng. in)[ J. Neural Eng.](Fabrizio Sciscenti et al 2025 J. Neural Eng. in)[ in](Fabrizio Sciscenti et al 2025 J. Neural Eng. in) press [https://doi.org/10.1088/1741-2552/adf579](https://doi.org/10.1088/1741-2552/adf579)  \nManuscript version: Accepted Manuscript  \nAccepted Manuscript is “the version of the article accepted for publication including all changes made as a result of the peer review process, and which may also include the addition to the article by IOP Publishing of a header, an article ID, a cover sheet and/or an ‘Accepted Manuscript’ watermark, but excluding any other editing, typesetting or other changes made by IOP Publishing and/or its licensors”  \nThis Accepted Manuscript is © 2025 The Author(s) . Published by IOP Publishing Ltd.  \nAs the Version of Record of this article is going to be / has been published on a gold open access basis under a CC BY 4.0 licence, this Accepted Manuscript is available for reuse under a CC BY 4.0 licence immediately.  \nEveryone is permitted to use all or part of the original content in this article, provided that they adhere to all the terms of the licence  \n[https://creativecommons.org/l](https://creativecommons.org/l)icences/by/4 .0  \nAlthough reasonable endeavours have been taken to obtain all necessary permissions from third parties to include their copyrighted content within this article, their full citation and copyright line may not be present in this Accepted Manuscript version. Before using any content from this article, please refer to the Version of Record on IOPscience once published for full citation and copyright details, as permissions may be required. All third party content is fully copyright protected and is not published on a gold open access basis under a CC BY licence, unless that is specifically stated in the figure caption in the Version of Record.  \nView the article online for updates and enhancements.  \nThis content was downloaded from IP address [130.192.232.214](130.192.232.214) on 30/07/2025 at 09:41  \nPage 1 of 15 AUTHOR SUBMITTED MANUSCRIPT-JNE-109079.R2  \n1 2  \n3 4  \n5 6  \n7 8  \n9  \n10  \n11  \n12  \n13  \n14  \n15  \n16  \n17  \n18  \n19  \n20  \n21  \n22  \n23  \n24  \n25  \n26  \n27  \n28  \n29  \n30  \n31  \n32  \n33  \n34  \n35  \n36  \n37  \n38  \n39  \n40  \n41  \n42  \n43  \n44  \n45  \n46  \n47  \n48  \n49  \n50  \n51  \n52  \n53  \n54  \n55  \n56  \n57  \n58  \n59  \n60  \nIOP Publishing Journal of Neural Engineering  \nJournal XX (XXXX) XXX","cbCaicMXgWnBtgKf","https://ap.wps.com/l/cbCaicMXgWnBtgKf","pdf",1404997,1,17,"English","en",105,"# Abstract\n## Objective\n## Approach\n## Main results","[{\"question\":\"What problem does ML-STIM address in STN intraoperative mapping?\",\"answer\":\"ML-STIM addresses the time-consuming and variable nature of trained-operator analysis of microelectrode recordings during STN identification for deep brain stimulation.\"},{\"question\":\"How is ML-STIM structured to classify the STN from MERs?\",\"answer\":\"ML-STIM includes MER preprocessing, adaptive artifact removal, feature extraction with correlation/ReliefF-based selection, and classification using a multi-layer perceptron (MLP).\"},{\"question\":\"How was the pipeline evaluated for generalizability?\",\"answer\":\"ML-STIM was trained and validated on a public dataset (46 patients) and tested on an independent dataset (36 patients) from a different surgical center; Dataset B is also made publicly available.\"}]","ML-STIM - Machine Learning for SubThalamic nucleus Intraoperative Mapping | PDF",1785730266,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"ml-stim-machine-learning-for-subthalamic-nucleus-intraoperative-mapping","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/ml-stim-machine-learning-for-subthalamic-nucleus-intraoperative-mapping/120472/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does ML-STIM address in STN intraoperative mapping?","Question",{"text":75,"@type":76},"ML-STIM addresses the time-consuming and variable nature of trained-operator analysis of microelectrode recordings during STN identification for deep brain stimulation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is ML-STIM structured to classify the STN from MERs?",{"text":80,"@type":76},"ML-STIM includes MER preprocessing, adaptive artifact removal, feature extraction with correlation/ReliefF-based selection, and classification using a multi-layer perceptron (MLP).",{"name":82,"@type":73,"acceptedAnswer":83},"How was the pipeline evaluated for generalizability?",{"text":84,"@type":76},"ML-STIM was trained and validated on a public dataset (46 patients) and tested on an independent dataset (36 patients) from a different surgical center; 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