{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T11:46:21Z","timestamp":1772797581447,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T00:00:00Z","timestamp":1600992000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014219","name":"National Science Fund for Distinguished Young Scholars","doi-asserted-by":"publisher","award":["81925020"],"award-info":[{"award-number":["81925020"]}],"id":[{"id":"10.13039\/501100014219","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFB1300302"],"award-info":[{"award-number":["2017YFB1300302"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81630051"],"award-info":[{"award-number":["81630051"]}],"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":["91648122"],"award-info":[{"award-number":["91648122"]}],"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":["81601565"],"award-info":[{"award-number":["81601565"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Tianjin Key Technology R&amp;D Program","award":["17ZXRGGX00020"],"award-info":[{"award-number":["17ZXRGGX00020"]}]},{"name":"Tianjin Key Technology R&amp;D Program","award":["16ZXHLSY00270"],"award-info":[{"award-number":["16ZXHLSY00270"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Motor function assessment is crucial in quantifying motor recovery following stroke. In the rehabilitation field, motor function is usually assessed using questionnaire-based assessments, which are not completely objective and require prior training for the examiners. Some research groups have reported that electroencephalography (EEG) data have the potential to be a good indicator of motor function. However, those motor function scores based on EEG data were not evaluated in a longitudinal paradigm. The ability of the motor function scores from EEG data to track the motor function changes in long-term clinical applications is still unclear. In order to investigate the feasibility of using EEG to score motor function in a longitudinal paradigm, a convolutional neural network (CNN) EEG model and a residual neural network (ResNet) EEG model were previously generated to translate EEG data into motor function scores. To validate applications in monitoring rehabilitation following stroke, the pre-established models were evaluated using an initial small sample of individuals in an active 14-week rehabilitation program. Longitudinal performances of CNN and ResNet were evaluated through comparison with standard Fugl\u2013Meyer Assessment (FMA) scores of upper extremity collected in the assessment sessions. The results showed good accuracy and robustness with both proposed networks (average difference: 1.22 points for CNN, 1.03 points for ResNet), providing preliminary evidence for the proposed method in objective evaluation of motor function of upper extremity in long-term clinical applications.<\/jats:p>","DOI":"10.3390\/s20195487","type":"journal-article","created":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T08:57:32Z","timestamp":1601024252000},"page":"5487","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["The Feasibility of Longitudinal Upper Extremity Motor Function Assessment Using EEG"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7623-5787","authenticated-orcid":false,"given":"Xin","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China"},{"name":"Menrva Research Group, Schools of Mechatronic Systems Engineering and Engineering Science, Simon Fraser University, Metro Vancouver, BC V5A 1S6, Canada"}]},{"given":"Ryan","family":"D\u2019Arcy","sequence":"additional","affiliation":[{"name":"Schools of Engineering Science and Computer Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada"}]},{"given":"Long","family":"Chen","sequence":"additional","affiliation":[{"name":"Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China"}]},{"given":"Minpeng","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China"}]},{"given":"Dong","family":"Ming","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China"},{"name":"Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China"},{"name":"Tianjin International Joint Research Center for Neural Engineering, Tianjin 300072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2309-9977","authenticated-orcid":false,"given":"Carlo","family":"Menon","sequence":"additional","affiliation":[{"name":"Menrva Research Group, Schools of Mechatronic Systems Engineering and Engineering Science, Simon Fraser University, Metro Vancouver, BC V5A 1S6, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13","DOI":"10.2340\/1650197771331","article-title":"The post-stroke hemiplegic patient. 1. a method for evaluation of physical performance","volume":"7","author":"Leyman","year":"1975","journal-title":"Scand. J. Rehabil. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.jphys.2013.12.012","article-title":"National Institutes of Health Stroke Scale (NIHSS)","volume":"60","author":"Kwah","year":"2014","journal-title":"J. Physiother."},{"key":"ref_3","first-page":"6","article-title":"The functional independence measure: A new tool for rehabilitation","volume":"1","author":"Keith","year":"1987","journal-title":"Adv. Clin. Rehabil."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1842","DOI":"10.1016\/S1388-2457(99)00141-8","article-title":"Event-related EEG\/MEG synchronization and desynchronization: Basic principles","volume":"110","author":"Pfurtscheller","year":"1999","journal-title":"Clin. Neurophysiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/S0167-8760(01)00178-7","article-title":"Event-related dynamics of cortical rhythms: Frequency-specific features and functional correlates","volume":"43","author":"Neuper","year":"2001","journal-title":"Int. J. Psychophysiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2017\/8276136","article-title":"Brain Symmetry Index in Healthy and Stroke Patients for Assessment and Prognosis","volume":"2017","author":"Anastasi","year":"2017","journal-title":"Stroke Res. Treat."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1016\/j.clinph.2009.01.021","article-title":"Delta-alpha ratio correlates with level of recovery after neurorehabilitation in patients with acquired brain injury","volume":"120","year":"2009","journal-title":"Clin. Neurophysiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1177\/1545968317697031","article-title":"Large-Scale Phase Synchrony Reflects Clinical Status after Stroke: An EEG Study","volume":"31","author":"Kawano","year":"2017","journal-title":"Neurorehabil. Neural Repair"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1177\/1545968320935820","article-title":"Electroencephalographic Phase Synchrony Index as a Biomarker of Poststroke Motor Impairment and Recovery","volume":"34","author":"Kawano","year":"2020","journal-title":"Neurorehabil. Neural Repair"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"036013","DOI":"10.1088\/1741-2552\/ab0b82","article-title":"Scoring upper-extremity motor function from EEG with artificial neural networks: A preliminary study","volume":"16","author":"Zhang","year":"2019","journal-title":"J. Neural Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1109\/TNSRE.2020.2978381","article-title":"Estimating Fugl-Meyer Upper Extremity Motor Score from Functional-Connectivity Measures","volume":"28","author":"Riahi","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"036007","DOI":"10.1088\/1741-2560\/10\/3\/036007","article-title":"Dynamically weighted ensemble classification for non-stationary EEG processing","volume":"10","author":"Liyanage","year":"2013","journal-title":"J. Neural Eng."},{"key":"ref_13","first-page":"1","article-title":"Online Covariate Shift Detection based Adaptive Brain-Computer Interface to Trigger Hand Exoskeleton Feedback for Neuro-Rehabilitation","volume":"XX","author":"Chowdhury","year":"2017","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016). Identity Mappings in Deep Residual Networks. Eur. Conf. Comput. Vis., 630\u2013645.","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Orand, A., Aksoy, E.E., Miyasaka, H., Levy, C., Zhang, X., and Menon, C. (2019). Bilateral tactile feedback-enabled training for stroke survivors using microsoft kinecttm. Sens. Switz., 19.","DOI":"10.3390\/s19163474"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/gps.4756","article-title":"A re-examination of Montreal Cognitive Assessment (MoCA) cutoff scores","volume":"33","author":"Carson","year":"2017","journal-title":"Int. J. Geriatr. Psychiatry"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/S0926-6410(96)00031-6","article-title":"Event-related desynchronisation of central beta-rhythms during brisk and slow self-paced finger movements of dominant and nondominant hand","volume":"4","author":"Pfurtscheller","year":"1996","journal-title":"Cogn. Brain Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"036023","DOI":"10.1088\/1741-2560\/10\/3\/036023","article-title":"Event-related desynchronization and synchronization quantification in motor-related EEG by Kolmogorov entropy","volume":"10","author":"Gao","year":"2013","journal-title":"J. Neural Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1097\/00001756-199212000-00006","article-title":"Simultaneous EEG 10 Hz desynchronization and 40 Hz synchronization during finger movements","volume":"3","author":"Pfurtscheller","year":"1992","journal-title":"Neuroreport"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.neuroimage.2005.12.003","article-title":"Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks","volume":"31","author":"Pfurtscheller","year":"2006","journal-title":"Neuroimage"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2015). Rethinking the Inception Architecture for Computer Vision. arXiv.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W., and Ball, T. (2017). Deep learning with convolutional neural networks for brain mapping and decoding of movement-related information from the human EEG. arXiv.","DOI":"10.1002\/hbm.23730"},{"key":"ref_24","unstructured":"Chollet, F. (2020, September 25). Keras. Github Repos. Available online: https:\/\/github.com\/keras-team\/keras."},{"key":"ref_25","unstructured":"Tensorflow (2020, September 25). TensorFlow. Available online: https:\/\/medium.com\/tensorflow\/tensorflow-at-google-i-o-2018-b6612840f59d."},{"key":"ref_26","unstructured":"Apache Spark (2020, September 25). Apache SparkTM\u2014Lightning-Fast Cluster Computing. Available online: http:\/\/spark.apache.org\/."},{"key":"ref_27","unstructured":"Bergstra, J., Bardenet, R., Bengio, Y., and K\u00e9gl, B. (2011, January 12). Algorithms for Hyper-Parameter Optimization. Proceedings of the 24th International Conference on Neural Information Processing Systems, Granada, Spain."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1177\/154596802401105171","article-title":"The Fugl-Meyer Assessment of Motor Recovery after Stroke: A Critical Review of Its Measurement Properties","volume":"16","author":"Gladstone","year":"2002","journal-title":"Neurorehabil. Neural Repair"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1080\/165019701750165916","article-title":"The responsiveness of the Action Research Arm test and the Fugl-Meyer Assessment scale in chronic stroke patients","volume":"33","author":"Beckerman","year":"2001","journal-title":"J. Rehabil. Med."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1093\/ptj\/73.7.447","article-title":"Reliability of the Fugl-Meyer assessment for testing motor performance in patients following stroke","volume":"73","author":"Sanford","year":"1993","journal-title":"Phys. Ther."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1177\/1545968308315999","article-title":"Psychometric Comparisons of 2 Versions of the Fugl-Meyer Motor Scale and 2 Versions of the Stroke Rehabilitation Assessment of Movement","volume":"22","author":"Hsueh","year":"2008","journal-title":"Neurorehabil. Neural Repair"},{"key":"ref_32","first-page":"917","article-title":"Minimal clinically important difference for the Fugl-Meyer assessment of the upper extremity in convalescent stroke patients with moderate to severe hemiparesis","volume":"31","author":"Hiragami","year":"2019","journal-title":"J. Phys. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/19\/5487\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:13:28Z","timestamp":1760177608000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/19\/5487"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,25]]},"references-count":32,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["s20195487"],"URL":"https:\/\/doi.org\/10.3390\/s20195487","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,25]]}}}