{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T02:12:24Z","timestamp":1773886344747,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T00:00:00Z","timestamp":1679961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["NIH\/NIBIB R01EB024343"],"award-info":[{"award-number":["NIH\/NIBIB R01EB024343"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["NIH\/NIA R01AG070135"],"award-info":[{"award-number":["NIH\/NIA R01AG070135"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["NIH\/NICHD R01HD085813"],"award-info":[{"award-number":["NIH\/NICHD R01HD085813"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01HD099846"],"award-info":[{"award-number":["R01HD099846"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>We introduce a new electroencephalogram (EEG) net, which will allow clinicians to monitor EEG while tracking head motion. Motion during MRI limits patient scans, especially of children with epilepsy. EEG is also severely affected by motion-induced noise, predominantly ballistocardiogram (BCG) noise due to the heartbeat. Methods: The MotoNet was built using polymer thick film (PTF) EEG leads and motion sensors on opposite sides in the same flex circuit. EEG\/motion measurements were made with a standard commercial EEG acquisition system in a 3 Tesla (T) MRI. A Kalman filtering-based BCG correction tool was used to clean the EEG in healthy volunteers. Results: MRI safety studies in 3 T confirmed the maximum heating below 1 \u00b0C. Using an MRI sequence with spatial localization gradients only, the position of the head was linearly correlated with the average motion sensor output. Kalman filtering was shown to reduce the BCG noise and recover artifact-clean EEG. Conclusions: The MotoNet is an innovative EEG net design that co-locates 32 EEG electrodes with 32 motion sensors to improve both EEG and MRI signal quality. In combination with custom gradients, the position of the net can, in principle, be determined. In addition, the motion sensors can help reduce BCG noise.<\/jats:p>","DOI":"10.3390\/s23073539","type":"journal-article","created":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T07:05:25Z","timestamp":1679987125000},"page":"3539","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["The MotoNet: A 3 Tesla MRI-Conditional EEG Net with Embedded Motion Sensors"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2510-1989","authenticated-orcid":false,"given":"Joshua","family":"Levitt","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2754-6594","authenticated-orcid":false,"given":"Andr\u00e9","family":"van der Kouwe","sequence":"additional","affiliation":[{"name":"Athinoula A. Martinos Center for Biomedical Engineering, Massachusetts General Hospital, Charlestown, MA 02129, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4908-2070","authenticated-orcid":false,"given":"Hongbae","family":"Jeong","sequence":"additional","affiliation":[{"name":"Athinoula A. Martinos Center for Biomedical Engineering, Massachusetts General Hospital, Charlestown, MA 02129, USA"}]},{"given":"Laura D.","family":"Lewis","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA"},{"name":"Athinoula A. Martinos Center for Biomedical Engineering, Massachusetts General Hospital, Charlestown, MA 02129, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4694-9698","authenticated-orcid":false,"given":"Giorgio","family":"Bonmassar","sequence":"additional","affiliation":[{"name":"Athinoula A. Martinos Center for Biomedical Engineering, Massachusetts General Hospital, Charlestown, MA 02129, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.neuroimage.2015.07.020","article-title":"Towards high-quality simultaneous EEG-fMRI at 7 T: Detection and reduction of EEG artifacts due to head motion","volume":"120","author":"Jorge","year":"2015","journal-title":"Neuroimage"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1097\/ACO.0b013e32833bb524","article-title":"Anaesthesia or sedation for MRI in children","volume":"23","author":"Goepfert","year":"2010","journal-title":"Curr. Opin. Anaesthesiol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mulert, C., and Lemieux, L. (2023). EEG-fMRI: Physiological Basis, Technique, and Applications, Springer. [2nd ed.].","DOI":"10.1007\/978-3-031-07121-8"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1578","DOI":"10.1007\/s00247-011-2205-1","article-title":"Prospective motion correction improves diagnostic utility of pediatric MRI scans","volume":"41","author":"Kuperman","year":"2011","journal-title":"Pediatr. Radiol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Placidi, G. (2012). MRI: Essentials for Innovative Technologies, CRC Press.","DOI":"10.1201\/b11868"},{"key":"ref_6","unstructured":"FDA (2017). FDA Drug Safety Communication: FDA approves label changes for use of general anesthetic and sedation drugs in young children, Drug Safety and Availability."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1002\/jmri.24850","article-title":"Motion artifacts in MRI: A complex problem with many partial solutions","volume":"42","author":"Zaitsev","year":"2015","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_8","first-page":"155","article-title":"Risk factors for the development of respiratory distress syndrome and transient tachypnoea in newborn infants","volume":"14","author":"Dani","year":"1999","journal-title":"Italian Group of Neonatal Pneumology. Eur. Respir. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1006\/nimg.2000.0599","article-title":"A method for removing imaging artifact from continuous EEG recorded during functional MRI","volume":"12","author":"Allen","year":"2000","journal-title":"Neuroimage"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1109\/42.650886","article-title":"Automatic correction of motion artifacts in magnetic resonance images using an entropy focus criterion","volume":"16","author":"Atkinson","year":"1997","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1002\/mrm.27783","article-title":"Retrospective correction of motion-affected MR images using deep learning frameworks","volume":"82","author":"Armanious","year":"2019","journal-title":"Magn. Reson. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1002\/mrm.24314","article-title":"Prospective motion correction in brain imaging: A review","volume":"69","author":"Maclaren","year":"2013","journal-title":"Magn. Reson. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1002\/mrm.28991","article-title":"Comparison of prospective and retrospective motion correction in 3D-encoded neuroanatomical MRI","volume":"87","author":"Slipsager","year":"2022","journal-title":"Magn. Reson. Med."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Slipsager, J.M., Ellegaard, A.H., Glimberg, S.L., Paulsen, R.R., Tisdall, M.D., Wighton, P., Van Der Kouwe, A., Marner, L., Henriksen, O.M., and Law, I. (2019). Markerless motion tracking and correction for PET, MRI, and simultaneous PET\/MRI. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0215524"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1016\/j.neuroimage.2006.01.039","article-title":"Magnetic resonance imaging of freely moving objects: Prospective real-time motion correction using an external optical motion tracking system","volume":"31","author":"Zaitsev","year":"2006","journal-title":"Neuroimage"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1002\/mrm.22797","article-title":"Head motion detection using FID navigators","volume":"66","author":"Kober","year":"2011","journal-title":"Magn. Reson. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1002\/mrm.25670","article-title":"Retrospective correction of involuntary microscopic head movement using highly accelerated fat image navigators (3D FatNavs) at 7T","volume":"75","author":"Gallichan","year":"2016","journal-title":"Magn. Reson. Med."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1002\/mrm.23228","article-title":"Volumetric navigators for prospective motion correction and selective reacquisition in neuroanatomical MRI","volume":"68","author":"Tisdall","year":"2012","journal-title":"Magn. Reson. Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1002\/mrm.22176","article-title":"PROMO: Real-time prospective motion correction in MRI using image-based tracking","volume":"63","author":"White","year":"2010","journal-title":"Magn. Reson. Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1002\/mrm.24845","article-title":"Prospective motion correction using inductively coupled wireless RF coils","volume":"70","author":"Ooi","year":"2013","journal-title":"Magn. Reson. Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.jneumeth.2008.07.017","article-title":"An open-source hardware and software system for acquisition and real-time processing of electrophysiology during high field MRI","volume":"175","author":"Purdon","year":"2008","journal-title":"J. Neurosci. Methods"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1002\/mrm.29251","article-title":"Tracking of rigid head motion during MRI using an EEG system","volume":"88","author":"Laustsen","year":"2022","journal-title":"Magn. Reson. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.neuroimage.2015.06.088","article-title":"Reference-free removal of EEG-fMRI ballistocardiogram artifacts with harmonic regression","volume":"128","author":"Krishnaswamy","year":"2016","journal-title":"Neuroimage"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.jneumeth.2014.06.021","article-title":"Ballistocardiogram artifact removal with a reference layer and standard EEG cap","volume":"233","author":"Luo","year":"2014","journal-title":"J. Neurosci. Methods"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1006\/nimg.2002.1125","article-title":"Motion and Ballistocardiogram Artifact Removal for Interleaved Recording of EEG and EPs during MRI","volume":"16","author":"Bonmassar","year":"2002","journal-title":"Neuroimage"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1610","DOI":"10.1109\/TMI.2019.2891774","article-title":"A Wireless Radio Frequency Triggered Acquisition Device (WRAD) for Self-Synchronised Measurements of the Rate of Change of the MRI Gradient Vector Field for Motion Tracking","volume":"38","author":"Meintjes","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.neuroimage.2016.03.034","article-title":"Ballistocardiogram artifact correction taking into account physiological signal preservation in simultaneous EEG-fMRI","volume":"135","author":"Abreu","year":"2016","journal-title":"Neuroimage"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"102303","DOI":"10.1118\/1.4895823","article-title":"Characterization of a dielectric phantom for high-field magnetic resonance imaging applications","volume":"41","author":"Duan","year":"2014","journal-title":"Med. Phys."},{"key":"ref_29","unstructured":"Hasgall, P., Di Gennaro, F., Baumgartner, C., Neufeld, E., Lloyd, B., Gosselin, M., Payne, D., Klingenb\u00f6ck, A., and Kuster, N. (2020, November 11). IT\u2019IS Database for Thermal and Electromagnetic Parameters of Biological Tissues. Available online: Itis.Swiss\/Database."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1109\/TEMC.2018.2840050","article-title":"Numerical and Experimental Analysis of Radiofrequency-Induced Heating Versus Lead Conductivity During EEG-MRI at 3 T","volume":"61","author":"Atefi","year":"2018","journal-title":"IEEE Trans. Electromagn. Compat."},{"key":"ref_31","unstructured":"Laustsen, M., Andersen, M., Lehmann, P.M., Xue, R., Madsen, K.H., and Hanson, L.G. (2018, January 16\u201321). Slice-wise motion tracking during simultaneous EEG-fMRI. Proceedings of the Joint Annual Meeting ISMRM-ESMRMB, Paris, France."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gholipour, A., Polak, M., van der Kouwe, A., Nevo, E., and Warfield, S.K. (September, January 30). Motion-robust MRI through real-time motion tracking and retrospective super-resolution volume reconstruction. Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA.","DOI":"10.1109\/IEMBS.2011.6091385"},{"key":"ref_33","unstructured":"De Cusatis, C., and Optical Society of America (1997). Handbook of Applied Photometry, Optical Society of America."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1006\/nimg.1998.0361","article-title":"Identification of EEG events in the MR scanner: The problem of pulse artifact and a method for its subtraction","volume":"8","author":"Allen","year":"1998","journal-title":"Neuroimage"},{"key":"ref_35","unstructured":"Center for Devices and Radiological Health (2021). Testing and Labeling Medical Devices for Safety in the Magnetic Resonance (MR) Environment."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1002\/(SICI)1521-186X(1996)17:3<195::AID-BEM5>3.0.CO;2-Z","article-title":"Radio frequency electromagnetic exposure: Tutorial review on experimental dosimetry","volume":"17","author":"Chou","year":"1996","journal-title":"Bioelectromagnetics"},{"key":"ref_37","unstructured":"NCRP (1981). Radiofrequency Electromagnetic Fields: Properties, Quantities and Units, Biophysical Interaction, and Measurement."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1278","DOI":"10.1109\/TBME.2005.847564","article-title":"Design and performance issues of RF coils utilized in ultra high field MRI: Experimental and numerical evaluations","volume":"52","author":"Ibrahim","year":"2005","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1002\/jmri.20041","article-title":"Temperature and SAR calculations for a human head within volume and surface coils at 64 and 300 MHz","volume":"19","author":"Collins","year":"2004","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_40","first-page":"1795","article-title":"MR imaging-related heating of deep brain stimulation electrodes: In Vitro study","volume":"23","author":"Finelli","year":"2002","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1002\/bem.96","article-title":"Empirical validation of SAR values predicted by FDTD modeling","volume":"23","author":"Gajsek","year":"2002","journal-title":"Bioelectromagnetics"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1002\/jmri.22843","article-title":"Simultaneous electroencephalography-functional MRI at 3 T: An analysis of safety risks imposed by performing anatomical reference scans with the EEG equipment in place","volume":"35","author":"Noth","year":"2012","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_43","unstructured":"Shellock, F.G. (2003). Magnetic Resonance Procedures: Health Effects and Safety, Amirsys."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1016\/j.neuroimage.2006.07.038","article-title":"EEG\/(f)MRI measurements at 7 Tesla using a new EEG cap (\u201cInkCap\u201d)","volume":"33","author":"Vasios","year":"2006","journal-title":"Neuroimage"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1748","DOI":"10.1109\/TEMC.2021.3097732","article-title":"Numerical simulation of the radiofrequency safety of 128-channel hd-EEG nets on a 29-month-old whole-body model in a 3 Tesla MRI","volume":"63","author":"Jeong","year":"2021","journal-title":"IEEE Trans. Electromagn. Compat."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1214","DOI":"10.1002\/mrm.27790","article-title":"Toward \u201cplug and play\u201d prospective motion correction for MRI by combining observations of the time varying gradient and static vector fields","volume":"82","author":"Meintjes","year":"2019","journal-title":"Magn. Reson. Med."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.neuroimage.2016.01.042","article-title":"Automatic EEG-assisted retrospective motion correction for fMRI (aE-REMCOR)","volume":"129","author":"Wong","year":"2016","journal-title":"Neuroimage"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1002\/mrm.26116","article-title":"Polymer thick film technology for improved simultaneous dEEG\/MRI recording: Safety and MRI data quality","volume":"77","author":"Poulsen","year":"2017","journal-title":"Magn. Reson. Med."},{"key":"ref_49","unstructured":"van der Kouwe, A., Jeong, H., Yang, Z., Straney, D., Frost, R., Lewis, L., and Bonmassar, G. (2022, January 7\u201312). The MotoNet: An MRI-Compatible EEG Net with Embedded Motion Sensors. Proceedings of the Joint annual Meeting ISMRM-ESMRMB-ISMRT, London, UK."},{"key":"ref_50","unstructured":"Haykin, S.S. (2002). Adaptive Filter Theory, Prentice Hall. [4th ed.]."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Levitt, J., Yang, Z., Williams, S., Espinosa, S., Garcia-Casal, A., and Lewis, L. (2022). EEG-LLAMAS: An open source, low latency, EEG-fMRI neurofeedback platform. bioRxiv.","DOI":"10.1101\/2022.11.21.515651"},{"key":"ref_52","unstructured":"Thrun, S., Burgard, W., and Fox, D. (2005). Probabilistic Robotics, MIT Press. Intelligent Robotics and Autonomous Agents."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/7\/3539\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:05:04Z","timestamp":1760123104000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/7\/3539"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,28]]},"references-count":52,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["s23073539"],"URL":"https:\/\/doi.org\/10.3390\/s23073539","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,28]]}}}