{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T02:43:36Z","timestamp":1772160216073,"version":"3.50.1"},"reference-count":23,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2021,11,22]],"date-time":"2021-11-22T00:00:00Z","timestamp":1637539200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100008982","name":"National Science Foundation of Sri Lanka","doi-asserted-by":"publisher","award":["1516107"],"award-info":[{"award-number":["1516107"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Eur J of Neuroscience"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Using motion capture data as a part of mobile brain\u2013body imaging (MoBI) recording has been increasing. With minimal linear algebra background, this paper explains how the rigid body transformation can be a useful preprocessing step for denoising and missing marker recovery. Such a transformation can provide insight and necessary\u2010and\u2010sufficient solutions requiring no assumption other than the minimum number of markers present. First, a simulation test using the empirical datasets from the\n                    <jats:italic>AudioMaze<\/jats:italic>\n                    project published from this journal's same volume demonstrates theoretical accuracy. The simulation results show that the rigid\u2010body method perfectly recovers missing markers on a rigid body if a minimum of three marker positions is available. Second, the same transformation is applied to the empirical dataset. Before preprocessing, the raw data show that 15\u201380% of data frames had all markers present for rigid\u2010body defined body parts. After using the rigid\u2010body correction, most body parts recovered full markers in 90\u201395% of the data frames. The result also suggests the necessity for performing across\u2010trial corrections for within\u2010participant (42% missing detected in one of the body parts) and across\u2010participants (11% missing). The discussion section introduces a solution and a performance summary for non\u2010rigid\u2010body marker correction using a neural network. Data support that the rigid body transformation is an intuitive and powerful correction method necessary for preprocessing motion capture data for neurocognitive experiments. The\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"#ejn15531-supitem-0001\">supporting information<\/jats:ext-link>\n                    section contains a URL link to Matlab code and example data.\n                  <\/jats:p>","DOI":"10.1111\/ejn.15531","type":"journal-article","created":{"date-parts":[[2021,11,20]],"date-time":"2021-11-20T04:47:37Z","timestamp":1637383657000},"page":"7946-7958","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Correcting whole\u2010body motion capture data using rigid body transformation"],"prefix":"10.1111","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4062-6679","authenticated-orcid":false,"given":"Makoto","family":"Miyakoshi","sequence":"first","affiliation":[{"name":"Swartz Center for Computational Neuroscience, Institute for Neural Computation University of California San Diego  La Jolla California USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,11,22]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/ejn.14866"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/0167-9457(91)90047-2"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/0021-9290(94)00116-L"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2012.6346236"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/ejn.15190"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/42.736021"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-012372560-8\/50002-4"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1137\/0717021"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/ejn.15152"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-86940-2_10"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2010.00202"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-97749-8"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1080\/10255841003664701"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2016.00306"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1115\/1.3662552"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-006-0080-9"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpsycho.2008.11.008"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.5040\/9781492595809"},{"key":"e_1_2_8_20_1","volume-title":"Robot modeling and control","author":"Spong M. 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