{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T05:02:22Z","timestamp":1778562142526,"version":"3.51.4"},"reference-count":44,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2019,6,27]],"date-time":"2019-06-27T00:00:00Z","timestamp":1561593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2016R1D1A1B03931672"],"award-info":[{"award-number":["2016R1D1A1B03931672"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Various convolutional neural network (CNN)-based approaches have been recently proposed to improve the performance of motor imagery based-brain-computer interfaces (BCIs). However, the classification accuracy of CNNs is compromised when target data are distorted. Specifically for motor imagery electroencephalogram (EEG), the measured signals, even from the same person, are not consistent and can be significantly distorted. To overcome these limitations, we propose to apply a capsule network (CapsNet) for learning various properties of EEG signals, thereby achieving better and more robust performance than previous CNN methods. The proposed CapsNet-based framework classifies the two-class motor imagery, namely right-hand and left-hand movements. The motor imagery EEG signals are first transformed into 2D images using the short-time Fourier transform (STFT) algorithm and then used for training and testing the capsule network. The performance of the proposed framework was evaluated on the BCI competition IV 2b dataset. The proposed framework outperformed state-of-the-art CNN-based methods and various conventional machine learning approaches. The experimental results demonstrate the feasibility of the proposed approach for classification of motor imagery EEG signals.<\/jats:p>","DOI":"10.3390\/s19132854","type":"journal-article","created":{"date-parts":[[2019,6,27]],"date-time":"2019-06-27T08:47:13Z","timestamp":1561625233000},"page":"2854","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":119,"title":["Motor Imagery EEG Classification Using Capsule Networks"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5287-7902","authenticated-orcid":false,"given":"Kwon-Woo","family":"Ha","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9313-6860","authenticated-orcid":false,"given":"Jin-Woo","family":"Jeong","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.neucom.2014.09.078","article-title":"Svm-based brain-machine interface for controlling a robot arm through four mental tasks","volume":"151","author":"Hortal","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1016\/j.proeng.2012.07.297","article-title":"A smart watch with embedded sensors to recognize objects, grasps and forearm gestures","volume":"41","author":"Morganti","year":"2012","journal-title":"Proced. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s11548-017-1677-3","article-title":"Auditory display as feedback for a novel eye-tracking system for sterile operating room interaction","volume":"13","author":"Black","year":"2018","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Hwang, C.-E., Lee, S.-H., and Jeong, J.-W. (2019). VisKit: Web-based interactive IoT management with deep visual object detection. J. Sens. Actuator Netw., 8.","DOI":"10.3390\/jsan8010012"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1109\/TCE.2019.2897758","article-title":"Watch & Do: A smart iot interaction system with object detection and gaze estimation","volume":"65","author":"Kim","year":"2019","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1940005","DOI":"10.1142\/S0219519419400050","article-title":"Convolutional long-short term memory networks model for long duration EEG signal classification","volume":"19","author":"Baloglu","year":"2019","journal-title":"J. Mech. Med. Biol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Y\u0131ld\u0131r\u0131m, \u00d6., Baloglu, U.B., and Acharya, U.R. (2018). A deep convolutional neural network model for automated identification of abnormal EEG signals. Neural Comput. Appl., 1\u201312.","DOI":"10.1007\/s00521-018-3889-z"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.3390\/s120201211","article-title":"Brain computer interfaces, a review","volume":"12","year":"2012","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.mayocp.2011.12.008","article-title":"Brain-computer interfaces in medicine","volume":"87","author":"Shih","year":"2012","journal-title":"Mayo Clin. Proc."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1038\/18581","article-title":"A spelling device for the paralysed","volume":"398","author":"Birbaumer","year":"1999","journal-title":"Nature"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1109\/86.847819","article-title":"Brain-computer interfaces based on the steady-state visual-evoked response","volume":"8","author":"Middendorf","year":"2000","journal-title":"IEEE Trans. Rehabil. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/TNSRE.2005.863842","article-title":"Steady-state somatosensory evoked potentials: Suitable brain signals for brain-computer interfaces?","volume":"14","author":"Scherer","year":"2006","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1016\/S0013-4694(97)00080-1","article-title":"EEG-based discrimination between imagination of right and left hand movement","volume":"103","author":"Pfurtscheller","year":"1997","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1016\/S1388-2457(98)00038-8","article-title":"Designing optimal spatial filters for single-trial EEG classification in a movement task","volume":"110","author":"Pfurtscheller","year":"1999","journal-title":"Clin. Neurophysiol."},{"key":"ref_16","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_17","unstructured":"Ang, K.K., Chin, Z.Y., Zhang, H., and Guan, C. (2008, January 1\u20138). Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface. Proceedings of the IEEE International Joint Conference on Neural Networks, Hong Kong, China."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"031005","DOI":"10.1088\/1741-2552\/aab2f2","article-title":"A review of classification algorithms for eeg-based brain-computer interfaces: A 10 year update","volume":"15","author":"Lotte","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_19","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). Imagenet Classification with Deep Convolutional Neural Networks. Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS), Lake Tahoe, NV, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"016003","DOI":"10.1088\/1741-2560\/14\/1\/016003","article-title":"A novel deep learning approach for classification of EEG motor imagery signals","volume":"14","author":"Tabar","year":"2017","journal-title":"J. Neural Eng."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Thomas, J., Maszczyk, T., Sinha, N., Kluge, T., and Dauwels, J. (2017, January 5\u20138). Deep Learning-Based Classification for Brain-Computer Interfaces. Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics, Banff, AB, Canada.","DOI":"10.1109\/SMC.2017.8122608"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kumar, S., Sharma, A., Mamun, K., and Tsunoda, T. (2016, January 5\u20136). A Deep Learning Approach for Motor Imagery EEG Signal Classification. Proceedings of the Asia-Pacific World Congress on Computer Science and Engineering (APWC), Nadi, Fiji.","DOI":"10.1109\/APWC-on-CSE.2016.017"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, J., Yan, C., and Gong, X. (2017, January 22\u201325). Deep Convolutional Neural Network for Decoding Motor Imagery Based Brain Computer Interface. Proceedings of the 2017 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Xiamen, China.","DOI":"10.1109\/ICSPCC.2017.8242581"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5391","DOI":"10.1002\/hbm.23730","article-title":"Deep learning with convolutional neural networks for EEG decoding and visualization","volume":"38","author":"Schirrmeister","year":"2017","journal-title":"Hum. Br. Mapp."},{"key":"ref_25","unstructured":"Sabour, S., Frosst, N., and Hinton, G.E. (2017, January 4\u20139). Dynamic Routing Between Capsules. Proceedings of the 31th International Conference on Neural Information Processing Systems (NIPS), Long Beach, CA, USA."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1109\/TNSRE.2007.906956","article-title":"Brain-computer communication: Motivation, aim, and impact of exploring a virtual apartment","volume":"15","author":"Leeb","year":"2007","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_27","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_28","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_29","unstructured":"Ang, K.K., and Quek, C. (2006, January 16\u201321). Rough Set-based Neuro-Fuzzy System. Proceedings of the 2006 IEEE International Joint Conference on Neural Network Proceedings, Vancouver, BC, Canada."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","article-title":"Recent advances in convolutional neural networks","volume":"77","author":"Gu","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_31","unstructured":"Nair, V., and Hinton, G. (2010, January 21\u201324). Rectified Linear Units Improve Restricted Boltzmann Machines. Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"056013","DOI":"10.1088\/1741-2552\/aace8c","article-title":"EEGNet: A compact convolutional neural network for EEG-based brain-computer interfaces","volume":"15","author":"Lawhern","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_33","unstructured":"Clevert, D.-A., Unterthiner, T., and Hochreiter, S. (2016, January 2\u20134). Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs). Proceedings of the 2016 International Conference on Learning Representations, San Juan, Puerto Rico."},{"key":"ref_34","first-page":"103","article-title":"EEG-based motor imagery classification using convolutional neural network","volume":"15","author":"Lee","year":"2017","journal-title":"J. Korean Inst. Inf. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Kwon, Y.-H., Shin, S.-B., Kim, S.-D., Kwon, Y.-H., Shin, S.-B., and Kim, S.-D. (2018). Electroencephalography based fusion two-dimensional (2D)-convolution neural networks (CNN) model for emotion recognition system. Sensors, 18.","DOI":"10.3390\/s18051383"},{"key":"ref_36","unstructured":"Klambauer, G., Unterthiner, T., Mayr, A., and Hochreiter, S. (2017, January 4\u20139). Self-Normalizing Neural Networks. Proceedings of the 31th International Conference on Neural Information Processing Systems (NIPS), Long Beach, CA, USA."},{"key":"ref_37","unstructured":"Mukhometzianov, R., and Carrillo, J. (arXiv, 2018). CapsNet comparative performance evaluation for image classification, arXiv."},{"key":"ref_38","unstructured":"Xi, E., Bing, S., and Jin, Y. (arXiv, 2017). Capsule Network Performance on Complex Data, arXiv."},{"key":"ref_39","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 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Singh, B., and Davis, L.S. (2018, January 18\u201322). An Analysis of Scale Invariance in Object Detection SNIP. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00377"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhou, P., Ni, B., Geng, C., Hu, J., and Xu, Y. (2018, January 18\u201322). Scale-Transferrable Object Detection. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00062"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_43","unstructured":"Gregor, K., Danihelka, I., Graves, A., Rezende, D., and Wierstra, D. (2015, January 6\u201311). DRAW: A Recurrent Neural Network For Image Generation. Proceedings of the 32nd International Conference on Machine Learning, Lille, France."},{"key":"ref_44","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2015, January 7\u20139). Neural Machine Translation by Jointly Learning to Align and Translate. Proceedings of the 3rd International Conference on Learning Representations (ICLR), San Diego, CA, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/13\/2854\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:01:37Z","timestamp":1760187697000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/13\/2854"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,27]]},"references-count":44,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["s19132854"],"URL":"https:\/\/doi.org\/10.3390\/s19132854","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,27]]}}}