{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T19:05:19Z","timestamp":1775156719407,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2020,12,19]],"date-time":"2020-12-19T00:00:00Z","timestamp":1608336000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institute of Information and Communications Technology Planning and Evaluation","award":["2017-0-00432"],"award-info":[{"award-number":["2017-0-00432"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study aimed to develop an intuitive gait-related motor imagery (MI)-based hybrid brain-computer interface (BCI) controller for a lower-limb exoskeleton and investigate the feasibility of the controller under a practical scenario including stand-up, gait-forward, and sit-down. A filter bank common spatial pattern (FBCSP) and mutual information-based best individual feature (MIBIF) selection were used in the study to decode MI electroencephalogram (EEG) signals and extract a feature matrix as an input to the support vector machine (SVM) classifier. A successive eye-blink switch was sequentially combined with the EEG decoder in operating the lower-limb exoskeleton. Ten subjects demonstrated more than 80% accuracy in both offline (training) and online. All subjects successfully completed a gait task by wearing the lower-limb exoskeleton through the developed real-time BCI controller. The BCI controller achieved a time ratio of 1.45 compared with a manual smartwatch controller. The developed system can potentially be benefit people with neurological disorders who may have difficulties operating manual control.<\/jats:p>","DOI":"10.3390\/s20247309","type":"journal-article","created":{"date-parts":[[2020,12,21]],"date-time":"2020-12-21T01:01:08Z","timestamp":1608512468000},"page":"7309","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":83,"title":["Developing a Motor Imagery-Based Real-Time Asynchronous Hybrid BCI Controller for a Lower-Limb Exoskeleton"],"prefix":"10.3390","volume":"20","author":[{"given":"Junhyuk","family":"Choi","sequence":"first","affiliation":[{"name":"Division of Bio-Medical Science &amp; Technology, KIST School, Korea University of Science and Technology, Seoul 02792, Korea"},{"name":"Center for Bionics, Biomedical Research Institute, Korea Institute of Science and Technology, Seoul 02792, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2731-3915","authenticated-orcid":false,"given":"Keun Tae","family":"Kim","sequence":"additional","affiliation":[{"name":"Center for Bionics, Biomedical Research Institute, Korea Institute of Science and Technology, Seoul 02792, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4200-0650","authenticated-orcid":false,"given":"Ji Hyeok","family":"Jeong","sequence":"additional","affiliation":[{"name":"Center for Bionics, Biomedical Research Institute, Korea Institute of Science and Technology, Seoul 02792, Korea"},{"name":"Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Korea"}]},{"given":"Laehyun","family":"Kim","sequence":"additional","affiliation":[{"name":"Center for Bionics, Biomedical Research Institute, Korea Institute of Science and Technology, Seoul 02792, Korea"}]},{"given":"Song Joo","family":"Lee","sequence":"additional","affiliation":[{"name":"Division of Bio-Medical Science &amp; Technology, KIST School, Korea University of Science and Technology, Seoul 02792, Korea"},{"name":"Center for Bionics, Biomedical Research Institute, Korea Institute of Science and Technology, Seoul 02792, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9527-0609","authenticated-orcid":false,"given":"Hyungmin","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Bio-Medical Science &amp; Technology, KIST School, Korea University of Science and Technology, Seoul 02792, Korea"},{"name":"Center for Bionics, Biomedical Research Institute, Korea Institute of Science and Technology, Seoul 02792, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1109\/5.939829","article-title":"Motor imagery and direct brain-computer communication","volume":"89","author":"Pfurtscheller","year":"2001","journal-title":"Proc. IEEE"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1109\/TRE.2000.847807","article-title":"Brain-Computer interface technology: A review of the first international meeting","volume":"8","author":"Wolpaw","year":"2000","journal-title":"IEEE Trans. Rehabil. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"21004","DOI":"10.1088\/1741-2552\/aaa8c0","article-title":"Brain-machine interfaces for controlling lower-limb powered robotic systems","volume":"15","author":"He","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"312","DOI":"10.3389\/fnhum.2018.00312","article-title":"EEG-Based BCI Control Schemes for Lower-Limb Assistive-Robots","volume":"12","author":"Tariq","year":"2018","journal-title":"Front. Hum. Neurosci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1109\/TNSRE.2006.875577","article-title":"The wadsworth BCI research and development program: At home with BCI","volume":"14","author":"Vaughan","year":"2006","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_6","unstructured":"Jeong, J.-H., Kwak, N.-S., Lee, M., and Lee, S. (2017, January 18\u201322). Decoding of walking Intention under Lower limb exoskeleton Environment using MRCP Feature. Proceedings of the GBCIC, Graz, Austria."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"56009","DOI":"10.1088\/1741-2560\/12\/5\/056009","article-title":"A lower limb exoskeleton control system based on steady state visual evoked potentials","volume":"12","author":"Kwak","year":"2015","journal-title":"J. Neural Eng."},{"key":"ref_8","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. Off. J. Int. Fed. Clin. Neurophysiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.jneumeth.2014.07.019","article-title":"DARPA-funded efforts in the development of novel brain-computer interface technologies","volume":"244","author":"Miranda","year":"2015","journal-title":"J. Neurosci. Methods"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1146\/annurev.bb.02.060173.001105","article-title":"Toward Direct Brain-Computer Communication","volume":"2","author":"Vidal","year":"1973","journal-title":"Annu. Rev. Biophys. Bioeng."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cheron, G., Duvinage, M., De Saedeleer, C., Castermans, T., Bengoetxea, A., Petieau, M., Seetharaman, K., Hoellinger, T., Dan, B., and Dutoit, T. (2012). From spinal central pattern generators to cortical network: Integrated BCI for walking rehabilitation. Neural Plast., 2012.","DOI":"10.1155\/2012\/375148"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/TNSRE.2014.2365697","article-title":"Design and Control of the MINDWALKER Exoskeleton","volume":"23","author":"Wang","year":"2015","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"31","DOI":"10.17691\/stm2017.9.3.04","article-title":"Exoskeleton Control System Based on Motor-Imaginary Brain\u2013Computer Interface","volume":"9","author":"Gordleeva","year":"2017","journal-title":"Sovrem. Tehnol. Med."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez-Vargas, J., Ib\u00e1\u00f1ez, J., Contreras-Vidal, J.L., van der Kooij, H., and Pons, J.L. (2016, January 18\u201321). Endogenous Control of Powered Lower-Limb Exoskeleton. Proceedings of the Wearable Robotics: Challenges and Trends, Segovia, Spain.","DOI":"10.1007\/978-3-319-46532-6"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"38524","DOI":"10.1109\/ACCESS.2018.2853628","article-title":"Implementation of a Brain-Computer Interface on a Lower-Limb Exoskeleton","volume":"6","author":"Wang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yu, G., Wang, J., Chen, W., and Zhang, J. (2017, January 19\u201321). EEG-based brain-controlled lower extremity exoskeleton rehabilitation robot. Proceedings of the 2017 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), Ningbo, China.","DOI":"10.1109\/ICCIS.2017.8274875"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1186\/1743-0003-10-111","article-title":"Brain-computer interface controlled robotic gait orthosis","volume":"10","author":"Do","year":"2013","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_18","first-page":"359","article-title":"Control of an Ambulatory Exoskeleton with a Brain\u2013Machine Interface for Spinal Cord Injury Gait Rehabilitation","volume":"10","author":"Rajasekaran","year":"2016","journal-title":"Front. Neurosci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"30383","DOI":"10.1038\/srep30383","article-title":"Long-Term Training with a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients","volume":"6","author":"Donati","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s10827-018-0701-0","article-title":"Motor imagery and mental fatigue: Inter-relationship and EEG based estimation","volume":"46","author":"Talukdar","year":"2019","journal-title":"J. Comput. Neurosci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1109\/TNSRE.2004.827220","article-title":"Continuous EEG classification during motor imagery-simulation of an asynchronous BCI","volume":"12","author":"Townsend","year":"2004","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Han, C.-H., M\u00fcller, K.-R., and Hwang, H.-J. (2020). Brain-Switches for Asynchronous Brain\u2013Computer Interfaces: A Systematic Review. Electronics, 9.","DOI":"10.3390\/electronics9030422"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Han, C.-H., Kim, E., and Im, C.-H. (2020). Development of a Brain-Computer Interface Toggle Switch with Low False-Positive Rate Using Respiration-Modulated Photoplethysmography. Sensors, 20.","DOI":"10.3390\/s20020348"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1292","DOI":"10.1109\/TNSRE.2019.2914916","article-title":"An Asynchronous Hybrid Spelling Approach Based on EEG\u2013EOG Signals for Chinese Character Input","volume":"27","author":"Yu","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ortiz, M., Ferrero, L., I\u00e1\u00f1ez, E., Azor\u00edn, J.M., and Contreras-Vidal, J.L. (2020). Sensory Integration in Human Movement: A New Brain-Machine Interface Based on Gamma Band and Attention Level for Controlling a Lower-Limb Exoskeleton. Front. Bioeng. Biotechnol., 8.","DOI":"10.3389\/fbioe.2020.00735"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1109\/TNSRE.2010.2040837","article-title":"Self-paced operation of an SSVEP-Based orthosis with and without an imagery-based \u201cbrain switch:\u201d A feasibility study towards a hybrid BCI","volume":"18","author":"Pfurtscheller","year":"2010","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng. Publ. IEEE Eng. Med. Biol. Soc."},{"key":"ref_27","first-page":"3","article-title":"The hybrid BCI","volume":"4","author":"Pfurtscheller","year":"2010","journal-title":"Front. Neurosci."},{"key":"ref_28","unstructured":"Kim, Y., Song, C., and Park, J. (2012, January 17\u201321). Development of actuation system for wearable robots using spiral spring. Proceedings of the 2012 12th International Conference on Control, Automation and Systems, Jeju Island, Korea."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1109\/86.895946","article-title":"Optimal spatial filtering of single trial EEG during imagined hand movement","volume":"8","author":"Ramoser","year":"2000","journal-title":"IEEE Trans. Rehabil. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"39","DOI":"10.3389\/fnins.2012.00039","article-title":"Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b","volume":"6","author":"Ang","year":"2012","journal-title":"Front. Neurosci."},{"key":"ref_31","unstructured":"Alvarez, L., Mejail, M., Gomez, L., and Jacobo, J. (2012, January 3\u20136). Recognition and Real-Time Detection of Blinking Eyes on Electroencephalographic Signals Using Wavelet Transform. Proceedings of the Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Buenos Aires, Argentina."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Choi, J., Kim, K., Lee, J., Lee, S.J., and Kim, H. (2020, January 18\u201320). Robust Semi-synchronous BCI Controller for Brain-Actuated Exoskeleton System. Proceedings of the 2020 8th International Winter Conference on Brain-Computer Interface (BCI), High1 Resort, Korea.","DOI":"10.1109\/BCI48061.2020.9061658"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/S0301-0511(03)00073-5","article-title":"Brain-computer interface (BCI) operation: Optimizing information transfer rates","volume":"63","author":"Mcfarland","year":"2003","journal-title":"Biol. Psychol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.jneumeth.2003.10.009","article-title":"EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis","volume":"134","author":"Delorme","year":"2004","journal-title":"J. Neurosci. Methods"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1869","DOI":"10.3389\/fpsyg.2015.01869","article-title":"Long-lasting cortical reorganization as the result of motor imagery of throwing a ball in a virtual tennis court","volume":"6","author":"Cebolla","year":"2015","journal-title":"Front. Psychol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1016\/j.clinph.2011.07.034","article-title":"Mu rhythm, visual processing and motor control","volume":"123","author":"Sabate","year":"2011","journal-title":"Clin. Neurophysiol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/s00221-005-0078-y","article-title":"Kinesthetic, but not visual, motor imagery modulates corticomotor excitability","volume":"168","author":"Stinear","year":"2006","journal-title":"Exp. Brain Res."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Tariq, M., Trivailo, P.M., and Simic, M. (2020). Mu-Beta event-related (de)synchronization and EEG classification of left-right foot dorsiflexion kinaesthetic motor imagery for BCI. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0230184"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1109\/TBME.2004.827086","article-title":"Noninvasive brain-actuated control of a mobile robot by human EEG","volume":"51","author":"Millan","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1109\/TRO.2012.2201310","article-title":"Toward Brain-Actuated Humanoid Robots: Asynchronous Direct Control Using an EEG-Based BCI","volume":"28","author":"Chae","year":"2012","journal-title":"IEEE Trans. Robot."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.ijleo.2016.10.117","article-title":"Single-trial EEG classification of motor imagery using deep convolutional neural networks","volume":"130","author":"Tang","year":"2017","journal-title":"Optik"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"51001","DOI":"10.1088\/1741-2552\/ab260c","article-title":"Deep learning-based electroencephalography analysis: A systematic review","volume":"16","author":"Roy","year":"2019","journal-title":"J. Neural Eng."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ha, K.-W., and Jeong, J.-W. (2019). Motor Imagery EEG Classification Using Capsule Networks. Sensors, 19.","DOI":"10.3390\/s19132854"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Jin, J., Xiao, R., Daly, I., Miao, Y., Wang, X., and Cichocki, A. (2020). Internal Feature Selection Method of CSP Based on L1-Norm and Dempster-Shafer Theory. IEEE Trans. Neural Networks Learn. Syst., 1\u201312.","DOI":"10.1109\/TNNLS.2020.3015505"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2153","DOI":"10.1109\/TNSRE.2020.3020975","article-title":"Bispectrum-Based Channel Selection for Motor Imagery Based Brain-Computer Interfacing","volume":"28","author":"Jin","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1152\/physrev.00027.2016","article-title":"Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation","volume":"97","author":"Lebedev","year":"2017","journal-title":"Physiol. Rev."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"S233","DOI":"10.1016\/j.pmrj.2018.05.028","article-title":"Brain Computer Interfaces in Rehabilitation Medicine","volume":"10","author":"Bockbrader","year":"2018","journal-title":"PM R"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"14","DOI":"10.3389\/fnhum.2018.00014","article-title":"EEG-Based Brain\u2013Computer Interfaces for Communication and Rehabilitation of People with Motor Impairment: A Novel Approach of the 21st Century","volume":"12","author":"Lazarou","year":"2018","journal-title":"Front. Hum. Neurosci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1109\/TNSRE.2003.814454","article-title":"Graz-BCI: State of the art and clinical applications","volume":"11","author":"Pfurtscheller","year":"2003","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1038\/nrn.2016.164","article-title":"Closed-loop brain training: The science of neurofeedback","volume":"18","author":"Sitaram","year":"2017","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1080\/17434440.2019.1574567","article-title":"Rehabilitative devices for a top-down approach","volume":"16","author":"Morone","year":"2019","journal-title":"Expert Rev. Med. Devices"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Cho, H.-Y., Kim, J.-S., and Lee, G.-C. (2012). Effects of motor imagery training on balance and gait abilities in post-stroke patients: A randomized controlled trial. Clin. Rehabil., 27.","DOI":"10.1177\/0269215512464702"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.neuropsychologia.2015.06.039","article-title":"Oscillations in the human brain during walking execution, imagination and observation","volume":"79","author":"Cevallos","year":"2015","journal-title":"Neuropsychologia"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/24\/7309\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:47:22Z","timestamp":1760179642000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/24\/7309"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,19]]},"references-count":53,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["s20247309"],"URL":"https:\/\/doi.org\/10.3390\/s20247309","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,19]]}}}