{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:00:50Z","timestamp":1760144450841,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T00:00:00Z","timestamp":1714003200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"JSPS KAKENHI","doi-asserted-by":"publisher","award":["JP20K11999"],"award-info":[{"award-number":["JP20K11999"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Brain\u2013computer interfaces (BCIs) allow information to be transmitted directly from the human brain to a computer, enhancing the ability of human brain activity to interact with the environment. In particular, BCI-based control systems are highly desirable because they can control equipment used by people with disabilities, such as wheelchairs and prosthetic legs. BCIs make use of electroencephalograms (EEGs) to decode the human brain\u2019s status. This paper presents an EEG-based facial gesture recognition method based on a self-organizing map (SOM). The proposed facial gesture recognition uses \u03b1, \u03b2, and \u03b8 power bands of the EEG signals as the features of the gesture. The SOM-Hebb classifier is utilized to classify the feature vectors. We utilized the proposed method to develop an online facial gesture recognition system. The facial gestures were defined by combining facial movements that are easy to detect in EEG signals. The recognition accuracy of the system was examined through experiments. The recognition accuracy of the system ranged from 76.90% to 97.57% depending on the number of gestures recognized. The lowest accuracy (76.90%) occurred when recognizing seven gestures, though this is still quite accurate when compared to other EEG-based recognition systems. The implemented online recognition system was developed using MATLAB, and the system took 5.7 s to complete the recognition flow.<\/jats:p>","DOI":"10.3390\/s24092741","type":"journal-article","created":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T08:08:32Z","timestamp":1714032512000},"page":"2741","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Electroencephalogram-Based Facial Gesture Recognition Using Self-Organizing Map"],"prefix":"10.3390","volume":"24","author":[{"given":"Takahiro","family":"Kawaguchi","sequence":"first","affiliation":[{"name":"Faculty of Engineering Science, Kansai University, Osaka 564-8680, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Koki","family":"Ono","sequence":"additional","affiliation":[{"name":"Faculty of Engineering Science, Kansai University, Osaka 564-8680, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2609-3500","authenticated-orcid":false,"given":"Hiroomi","family":"Hikawa","sequence":"additional","affiliation":[{"name":"Faculty of Engineering Science, Kansai University, Osaka 564-8680, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1109\/TCBB.2021.3052811","article-title":"EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications","volume":"18","author":"Gu","year":"2021","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Liu, H., Zhang, Y., Li, Y., and Kong, X. (2021). Review on Emotion Recognition Based on Electroencephalography. Front. Comput. Neurosci., 15.","DOI":"10.3389\/fncom.2021.758212"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1109\/TSMCC.2012.2219046","article-title":"EEG-Based Brain-Controlled Mobile Robots: A Survey","volume":"43","author":"Bi","year":"2013","journal-title":"IEEE Trans. Hum. Mach. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1109\/THMS.2018.2830647","article-title":"A Survey on Unmanned Aerial Vehicle Remote Control Using Brain Computer Interface","volume":"48","author":"Nourmohammadi","year":"2018","journal-title":"IEEE Trans. Hum. Mach. Syst."},{"key":"ref_5","first-page":"122","article-title":"Implementing remote presence using quadcopter control by a non-invasive BCI device","volume":"3","author":"Lin","year":"2015","journal-title":"Comput. Sci. Inf. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kohonen, T. (2001). Self-Organizing Maps, Springer.","DOI":"10.1007\/978-3-642-56927-2"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.ins.2013.10.002","article-title":"Improving MR Brain Image Segmentation Using Self-Organising Maps and Entropy-Gradient Clustering","volume":"262","author":"Ortiz","year":"2014","journal-title":"Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tang, Y., Fang, B., and Shang, Z. (2014, January 20\u201321). Real-Time Object Tracking in Video Pictures Based on Self-Organizing Map and Image Segmentation. Proceedings of the 2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference, Chongqing, China.","DOI":"10.1109\/ITAIC.2014.7065113"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2595","DOI":"10.1109\/TNNLS.2017.2699674","article-title":"An Information-Theoretic-Cluster Visualization for Self-Organizing Maps","volume":"29","author":"Wunsch","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1457","DOI":"10.1007\/s11554-020-00957-0","article-title":"Real-Time Automated Video Highlight Generation with Dual-Stream Hierarchical Growing Self-Organizing Maps","volume":"18","author":"Gunawardena","year":"2020","journal-title":"J. Real-Time Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1109\/TCSVT.2014.2335831","article-title":"Novel FPGA Implementation of Hand Sign Recognition System With SOM-Hebb Classifier","volume":"25","author":"Hikawa","year":"2015","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_12","unstructured":"Kohonen, T. (1996). The Self-Organizing Map, Possible Model of Brain Maps. Med. Biol. Eng. Comput., 34."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, G., Zhou, Y., Li, Z., and Li, Y. (2021, January 25\u201327). EEG signal feature reduction and channel selection method in hand gesture recognition BCI system. Proceedings of the 2021 International Conference on Computer Engineering and Application (ICCEA), Kunming, China.","DOI":"10.1109\/ICCEA53728.2021.00062"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Mohseni Salehi, S.S., Moghadamfalahi, M., Quivira, F., Piers, A., Nezamfar, H., and Erdogmus, D. (2017, January 11\u201315). Decoding complex imagery hand gestures. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju, Republic of Korea.","DOI":"10.1109\/EMBC.2017.8037480"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Schreiner, L., Sieghartsleitner, S., Mayr, K., Pretl, H., and Guger, C. (2023, January 24\u201327). Hand gesture decoding using ultra-high-density EEG. Proceedings of the 2023 11th International IEEE\/EMBS Conference on Neural Engineering (NER), Baltimore, MD, USA.","DOI":"10.1109\/NER52421.2023.10123901"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3113","DOI":"10.1109\/JSEN.2019.2956998","article-title":"Classification of Hand Movements From EEG Using a Deep Attention-Based LSTM Network","volume":"20","author":"Zhang","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1034","DOI":"10.1109\/TBME.2004.827072","article-title":"BCI2000: A general-purpose brain\u2013computer interface (BCI) system","volume":"51","author":"Schalk","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Shilaskar, S., Talwekar, S., Bhatlawande, S., Singh, S., and Jalnekar, R. (2022, January 15\u201317). An Electroencephalogram Based Detection of Hook and Span Hand Gestures. Proceedings of the 2022 IEEE Pune Section International Conference (PuneCon), Pune, India.","DOI":"10.1109\/PuneCon55413.2022.10014983"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kapeller, C., Schneider, C., Kamada, K., Ogawa, H., Kunii, N., Ortner, R., Pruckl, R., and Guger, C. (2014, January 26\u201330). Single trial detection of hand poses in human ECoG using CSP based feature extraction. Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA.","DOI":"10.1109\/EMBC.2014.6944648"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Pradeepkumar, J., Anandakumar, M., Kugathasan, V., Lalitharatne, T.D., De Silva, A.C., and Kappel, S.L. (2021, January 1\u20135). Decoding of Hand Gestures from Electrocorticography with LSTM Based Deep Neural Network. Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Mexico.","DOI":"10.1109\/EMBC46164.2021.9630958"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Miller, K.J., Hermes, D., Honey, C.J., Hebb, A.O., Ramsey, N.F., Knight, R.T., Ojemann, J.G., and Fetz, E.E. (2012). Human Motor Cortical Activity Is Selectively Phase-Entrained on Underlying Rhythms. PLoS Comput. Biol., 8.","DOI":"10.1371\/journal.pcbi.1002655"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liao, K., Xiao, R., Gonzalez, J., and Ding, L. (2014). Decoding Individual Finger Movements from One Hand Using Human EEG Signals. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0085192"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neucom.2012.10.041","article-title":"An EEG-based brain\u2013computer interface for dual task driving detection","volume":"129","author":"Wang","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1590\/2446-4740.0753","article-title":"A self-organizing maps classifier structure for brain computer interfaces","volume":"31","author":"Bueno","year":"2015","journal-title":"Res. Biomed. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1587\/transfun.E101.A.499","article-title":"Off-Chip Training with Additive Perturbation for FPGA-Based Hand Sign Recognition System","volume":"E101-A","author":"Hikawa","year":"2018","journal-title":"IEICE Trans. Fundam. Electron. Commun. Comput. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/9\/2741\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:32:58Z","timestamp":1760106778000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/9\/2741"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,25]]},"references-count":25,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["s24092741"],"URL":"https:\/\/doi.org\/10.3390\/s24092741","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2024,4,25]]}}}