[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128488-en":3,"doc-seo-128488-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},128488,13056712833777,"Logic","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Too much information is no information - how machine learning and feature selection could help in understanding the motor control of pointing","This study develops machine learning methods for multivariate analysis in motor control research, where large datasets are often analyzed univariately. High-dimensional electromyogram (EMG) vectors from arm and trunk muscles were recorded while subjects pointed at angles above and below the gravity-neutral horizontal plane. Linear Discriminant Analysis (LDA) performed binary classifications by pointing angle, yielding a composite index of muscular adjustments. A post-classification feature selection strategy compared class representations to identify feature combinations most altered by task conditions.","TYPE Original Research PUBLISHED 20 July 2023  \nDOI 10. 3389/fdata.2023.921355  \nOPEN ACCESS  \nEDITED BY  \nGuy Cheron,  \nUniversité libre de Bruxelles, Belgium  \nREVIEWED BY  \nHui Zhou,  \nNanjing University of Science and Technology, China  \nSabir Jacquir,  \nUniversité Paris-Saclay, France  \n*CORRESPONDENCE  \nElizabeth Thomas  \n [Elizabeth.Thomas@u-bourgogne.fr](Elizabeth.Thomas@u-bourgogne.fr)  \nRECEIVED 15 April 2022  \nACCEPTED 16 June 2023  \nPUBLISHED 20 July 2023  \nCITATION  \nThomas E, Ali FB, Tolambiya A, Chambellant Fand Gaveau J (2023) Too much information is no information: how machine learning and feature selection could help in understanding the motor control of pointing.  \nFront. Big Data 6:921355 .  \ndoi: 10.3389/fdata.2023.921355  \nCOPYRIGHT  \n© 2023 Thomas, Ali, Tolambiya, Chambellant and Gaveau. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nToo much information is no information: how machine learning and feature selection could help in understanding the motor control of pointing  \nElizabeth Thomas1*, Ferid Ben Ali2 , Arvind Tolambiya3 , Florian Chambellant1 and Jérémie Gaveau1  \n1 INSERMU1093, UFR STAPS, Université de Bourgogne Franche Comté, Dijon, France, 2 School of Engineering and Computer Science, University of Hertfordshire, Hatfield, United Kingdom, 3Applied Intelligence Hub, Accenture Solutions Private Ltd., Hyderabad, Telangana, India  \nThe aim of this study was to develop the use of Machine Learning techniques as a means of multivariate analysis in studies of motor control. These studies generate a huge amount of data, the analysis of which continues to be largely univariate. We propose the use of machine learning classification and featureselection as a means of uncovering feature combinations that are altered between conditions. High dimensional electromyogram (EMG) vectors were generated as several arm and trunk muscles were recorded while subjects pointed at various angles above and below the gravity neutral horizontal plane. We used Linear Discriminant Analysis (LDA) to carry out binary classifications between the EMG vectors for pointing at a particular angle, vs. pointing at the gravity neutral direction. Classification success provided a composite index of muscular adjustments for various task constraints—in this case, pointing angles. In order to find the combination of features that were significantly altered between task conditions, we conducted a post classification feature selection i.e., investigated which combination of features had allowed for the classification. Feature selection was done by comparing the representations of each category created by LDA for the classification. In other words computing the di􀀀erence between the representations of each class. We propose that this approach will help with comparing high dimensional EMG patterns in two ways; (i) quantifying the e􀀀ectsof the entire pattern rather than using single arbitrarily defined variables and (ii) identifying the parts of the patterns that convey the most information regarding the investigated e􀀀ects.  \nKEYWORDS  \nmotor control, machine learning, feature selection, pointing, explainable machine learning  \n1. Introduction  \nMovement takes place through the contractions of several muscles which then cause the displacement of several body segments. Much pain and energy therefore goes into the simultaneous collection of the variables connected with motor control studies. Curiously despite the energy invested in the synchronization of the data collection and the big data tables created by such experiments, the analysis of it largely take","cbCaifYHn5OLOFST","https://ap.wps.com/l/cbCaifYHn5OLOFST","pdf",1437583,1,14,"English","en",105,"# Introduction\n## Machine learning for multivariate motor control analysis\n## Feature selection for identifying altered feature combinations\n## EMG data collection and classification approach","[{\"question\":\"What problem does the study target in motor control research?\",\"answer\":\"It targets the tendency to analyze motor control experiments largely in a univariate way despite the creation of large datasets.\"},{\"question\":\"How were the EMG data and conditions defined?\",\"answer\":\"Subjects produced pointing movements at various angles above and below the gravity-neutral horizontal plane while multiple arm and trunk muscles were recorded as high-dimensional EMG vectors.\"},{\"question\":\"How does the method identify which features change across task conditions?\",\"answer\":\"The approach uses LDA to classify EMG vectors, then performs post-classification feature selection by comparing representations across the categories to quantify differences and determine which feature combinations are altered.\"}]","Too much information is no information - 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