{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T10:50:01Z","timestamp":1785408601282,"version":"3.56.0"},"reference-count":58,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T00:00:00Z","timestamp":1775520000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003627","name":"Rural Development Administration, Republic of Korea","doi-asserted-by":"publisher","award":["RS-2026-25519175"],"award-info":[{"award-number":["RS-2026-25519175"]}],"id":[{"id":"10.13039\/501100003627","id-type":"DOI","asserted-by":"publisher"}]},{"award":["RS-2026-25519175"],"award-info":[{"award-number":["RS-2026-25519175"]}],"id":[{"id":"https:\/\/ror.org\/03xs9yg50","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT","doi-asserted-by":"publisher","award":["IITP-2026-RS-2024-00438239"],"award-info":[{"award-number":["IITP-2026-RS-2024-00438239"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"award":["IITP-2026-RS-2024-00438239"],"award-info":[{"award-number":["IITP-2026-RS-2024-00438239"]}],"id":[{"id":"https:\/\/ror.org\/01wpjm123","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A brain\u2013computer interface (BCI) enables direct communication between the brain and external devices by translating neural activity into executable control commands. Among electroencephalography (EEG)-based paradigms, steady-state visual evoked potential (SSVEP) is widely adopted due to its high signal-to-noise ratio, robustness, and minimal calibration requirements. While SSVEP-based spellers have been extensively investigated, many existing systems rely on high-channel-density EEG recordings and computationally complex processing pipelines, and are primarily designed for alphabetic input structures. In this study, we present an SSVEP-based Korean speller that integrates the Cheonjiin keyboard layout to support intuitive composition of Hangul syllables. The proposed system adopts a simple configuration, employing only five visual stimulation frequencies (6.67\u201312 Hz) and two occipital EEG channels (O1 and O2), with real-time frequency recognition performed using canonical correlation analysis (CCA) within a 1.5 s sliding window. EEG signals were acquired at 200 Hz using an OpenBCI Ganglion board, band-pass filtered (5\u201345 Hz), and processed with harmonic sinusoidal reference templates for multi-frequency classification. The proposed interface generates five control commands (up, down, left, right, and select), enabling directional cursor navigation and character confirmation on a 4 \u00d7 4 virtual Cheonjiin keyboard. Experimental validation with three healthy participants demonstrated an average classification accuracy of approximately 82% and an information transfer rate (ITR) of 31.2 bits\/min. Frequency-domain analysis revealed clear spectral peaks at the stimulation frequencies and their harmonics, indicating reliable SSVEP responses. The proposed system employs a simple two-channel configuration integrated with a Korean language-specific input structure, demonstrating that reliable SSVEP-based communication can be realized without computationally intensive algorithms or high-cost EEG acquisition systems. These findings demonstrate that reliable SSVEP-based communication can be achieved using a low-channel configuration without reliance on high-cost EEG equipment.<\/jats:p>","DOI":"10.3390\/s26072265","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T08:09:00Z","timestamp":1775549340000},"page":"2265","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Cheonjiin Layout Mental Speller: Developing a Simple and Cost-Effective EEG-Based Brain\u2013Computer Interface System"],"prefix":"10.3390","volume":"26","author":[{"given":"Ji Won","family":"Ahn","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Yonsei University, Wonju 26493, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gi Yeon","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Yonsei University, Wonju 26493, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seong-Wan","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Agricultural Biology, National Institute of Agricultural Sciences, Rural Development Administration, Wanju 55365, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Young-Seek","family":"Seok","sequence":"additional","affiliation":[{"name":"Gangwon-do Agricultural Product Registered Seed Station, Chuncheon 24410, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyung-Min","family":"Byun","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin 17104, Republic of Korea"},{"name":"Department of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin 17104, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seung Ho","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Yonsei University, Wonju 26493, Republic of Korea"},{"name":"Department of Integrative Medicine, Major in Digital Healthcare, Yonsei University College of Medicine, Seoul 06229, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wolpaw, J.R. (2007, January 15\u201317). Brain-computer interfaces (BCIs) for communication and control. Proceedings of the 9th International ACM SIGACCESS Conference on Computers and Accessibility, Tempe, AZ, USA.","DOI":"10.1145\/1296843.1296845"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wolpaw, J.R., and Wolpaw, E.W. (2013). Brain Computer Interfaces, Principles and Practise, Springer.","DOI":"10.1093\/acprof:oso\/9780195388855.001.0001"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1016\/j.tins.2006.07.004","article-title":"Brain\u2013machine interfaces: Past, present and future","volume":"29","author":"Lebedev","year":"2006","journal-title":"Trends Neurosci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/BF00313105","article-title":"Varieties of the locked-in syndrome","volume":"221","author":"Bauer","year":"1979","journal-title":"J. Neurol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1016\/S0079-6123(05)50034-7","article-title":"The locked-in syndrome: What is it like to be conscious but paralyzed and voiceless?","volume":"150","author":"Laureys","year":"2005","journal-title":"Prog. Brain Res."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1145\/1941487.1941506","article-title":"Brain-computer interfaces for communication and control","volume":"54","author":"McFarland","year":"2011","journal-title":"Commun. ACM"},{"key":"ref_8","unstructured":"Regan, D. (1989). Human brain electrophysiology. Evoked Potentials and Evoked Magnetic Fields in Science and Medicine, Elsevier."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1167\/15.6.4","article-title":"The steady-state visual evoked potential in vision research: A review","volume":"15","author":"Norcia","year":"2015","journal-title":"J. Vis."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"258","DOI":"10.26599\/BSA.2022.9050022","article-title":"Review of brain\u2013computer interface based on steady-state visual evoked potential","volume":"8","author":"Liu","year":"2022","journal-title":"Brain Sci. Adv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1109\/TBME.2002.803536","article-title":"Design and implementation of a brain-computer interface with high transfer rates","volume":"49","author":"Cheng","year":"2002","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1109\/TNSRE.2003.814449","article-title":"A BCI-based environmental controller for the motion-disabled","volume":"11","author":"Gao","year":"2003","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"14708","DOI":"10.1038\/s41598-018-32283-8","article-title":"A high-speed SSVEP-based BCI using dry EEG electrodes","volume":"8","author":"Xing","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Nakanishi, M., Wang, Y., Wang, Y.-T., and Jung, T.-P. (2015). A comparison study of canonical correlation analysis based methods for detecting steady-state visual evoked potentials. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0140703"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1109\/MEMB.2008.923958","article-title":"Brain-computer interfaces based on visual evoked potentials","volume":"27","author":"Wang","year":"2008","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1109\/TNSRE.2006.875576","article-title":"A practical VEP-based brain-computer interface","volume":"14","author":"Wang","year":"2006","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"E6058","DOI":"10.1073\/pnas.1508080112","article-title":"High-speed spelling with a noninvasive brain\u2013computer interface","volume":"112","author":"Chen","year":"2015","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1746","DOI":"10.1109\/TNSRE.2016.2627556","article-title":"A benchmark dataset for SSVEP-based brain\u2013computer interfaces","volume":"25","author":"Wang","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2610","DOI":"10.1109\/TBME.2006.886577","article-title":"Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs","volume":"53","author":"Lin","year":"2006","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"046008","DOI":"10.1088\/1741-2560\/12\/4\/046008","article-title":"Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain\u2013computer interface","volume":"12","author":"Chen","year":"2015","journal-title":"J. Neural Eng."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Xu, D., Tang, F., Li, Y., Zhang, Q., and Feng, X. (2023). An analysis of deep learning models in SSVEP-based BCI: A survey. Brain Sci., 13.","DOI":"10.3390\/brainsci13030483"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"056014","DOI":"10.1088\/1741-2552\/ac8dc5","article-title":"An efficient CNN-LSTM network with spectral normalization and label smoothing technologies for SSVEP frequency recognition","volume":"19","author":"Pan","year":"2022","journal-title":"J. Neural Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"066031","DOI":"10.1088\/1741-2552\/aae5d8","article-title":"Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials","volume":"15","author":"Waytowich","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hong, K.-S., and Khan, M.J. (2017). Hybrid brain\u2013computer interface techniques for improved classification accuracy and increased number of commands: A review. Front. Neurorobot., 11.","DOI":"10.3389\/fnbot.2017.00035"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, J., Gao, S., Zhou, K., Cheng, Y., and Mao, S. (2023). An online hybrid BCI combining SSVEP and EOG-based eye movements. Front. Hum. Neurosci., 17.","DOI":"10.3389\/fnhum.2023.1103935"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"117180","DOI":"10.1016\/j.eswa.2022.117180","article-title":"A comprehensive benchmark dataset for SSVEP-based hybrid BCI","volume":"200","author":"Sadeghi","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1007\/s13534-025-00469-5","article-title":"A review of hybrid EEG-based multimodal human\u2013computer interfaces using deep learning: Applications, advances, and challenges","volume":"15","author":"Lee","year":"2025","journal-title":"Biomed. Eng. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s10055-025-01286-y","article-title":"Next-generation BCI spellers: A comparative study of ERP responses and fatigue in mixed, virtual, and desktop realities","volume":"30","author":"Arif","year":"2025","journal-title":"Virtual Real."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"110062","DOI":"10.1016\/j.compbiomed.2025.110062","article-title":"Design of asynchronous low-complexity SSVEP-based brain control interface speller","volume":"190","author":"Preetha","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"105042","DOI":"10.1016\/j.compbiomed.2021.105042","article-title":"A CNN-based multi-target fast classification method for AR-SSVEP","volume":"141","author":"Zhao","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Li, R., Zhang, Y., Fan, G., Li, Z., Li, J., Fan, S., Lou, C., and Liu, X. (2023). Design and implementation of high sampling rate and multichannel wireless recorder for EEG monitoring and SSVEP response detection. Front. Neurosci., 17.","DOI":"10.3389\/fnins.2023.1193950"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1016\/j.neuroimage.2010.06.048","article-title":"Single-trial analysis and classification of ERP components\u2014A tutorial","volume":"56","author":"Blankertz","year":"2011","journal-title":"NeuroImage"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"107404","DOI":"10.1016\/j.bspc.2024.107404","article-title":"Subject-specific CNN model with parameter-based transfer learning for SSVEP detection","volume":"103","author":"Ji","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.bbe.2026.03.002","article-title":"Subject-specific optimization of visual stimulation enhances the performance of a flicker-free SSVEP-based BCI system","volume":"46","author":"Ming","year":"2026","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"031005","DOI":"10.1088\/1741-2552\/aab2f2","article-title":"A review of classification algorithms for EEG-based brain\u2013computer interfaces: A 10 year update","volume":"15","author":"Lotte","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1007\/s11517-023-03000-z","article-title":"Assessment of visual fatigue in SSVEP-based brain-computer interface: A comprehensive study","volume":"62","author":"Diez","year":"2024","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Rezeika, A., Benda, M., Stawicki, P., Gembler, F., Saboor, A., and Volosyak, I. (2018). Brain\u2013computer interface spellers: A review. Brain Sci., 8.","DOI":"10.3390\/brainsci8040057"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"129999","DOI":"10.1016\/j.eswa.2025.129999","article-title":"Mind-Pinyin Speller: A Non-Invasive Brain-Computer Interface for Efficient Chinese Character Input Using EEG-Based Imagined Handwriting","volume":"299","author":"Wu","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Borirakarawin, M., and Punsawad, Y. (2022). Event-related potential-based brain\u2013computer interface using the Thai vowels\u2019 and numerals\u2019 auditory stimulus pattern. Sensors, 22.","DOI":"10.3390\/s22155864"},{"key":"ref_40","unstructured":"Kim-Renaud, Y.-K. (2021). The Korean Alphabet: Its History and Structure, University of Hawaii Press."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lee, K.-M., and Ramsey, S.R. (2011). A History of the Korean Language, Cambridge University Press.","DOI":"10.1017\/CBO9780511974045"},{"key":"ref_42","first-page":"57","article-title":"Brain-Computer Interface Spellers: A Review","volume":"8","author":"Saboor","year":"2021","journal-title":"Brain Sci."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Lee, T.-H., Kam, T.-E., and Kim, S.-P. (2011, January 16\u201318). A P300-Based Hangul Input System with a Hierarchical Stimulus Presentation Paradigm. Proceedings of the 2011 International Workshop on Pattern Recognition in NeuroImaging, Seoul, Republic of Korea.","DOI":"10.1109\/PRNI.2011.20"},{"key":"ref_44","unstructured":"Lee, S.-H. (2011). Method for Inputting Characters in a Mobile Communication Terminal and Mobile Communication Terminal Using the Same. (KR101066263B1), Korean Patent, Available online: https:\/\/patents.google.com\/patent\/KR101066263B1."},{"key":"ref_45","unstructured":"OpenBCI (2026, February 24). Ganglion Board Technical Specifications. Available online: https:\/\/docs.openbci.com\/Ganglion\/GanglionSpecs\/."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.1162\/0899766042321814","article-title":"Canonical correlation analysis: An overview with application to learning methods","volume":"16","author":"Hardoon","year":"2004","journal-title":"Neural Comput."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"046002","DOI":"10.1088\/1741-2560\/6\/4\/046002","article-title":"An online multi-channel SSVEP-based brain\u2013computer interface using a canonical correlation analysis method","volume":"6","author":"Bin","year":"2009","journal-title":"J. Neural Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TCDS.2020.3007453","article-title":"Transfer learning for EEG-based brain\u2013computer interfaces: A review of progress made since 2016","volume":"14","author":"Wu","year":"2020","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Chen, Y., Shi, X., De Silva, V., and Dogan, S. (2024). Steady-State Visual Evoked Potential-Based Brain\u2013Computer Interface System for Enhanced Human Activity Monitoring and Assessment. Sensors, 24.","DOI":"10.3390\/s24217084"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1450019","DOI":"10.1142\/S0129065714500191","article-title":"A high-speed brain speller using steady-state visual evoked potentials","volume":"24","author":"Nakanishi","year":"2014","journal-title":"Int. J. Neural Syst."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Liu, H., Wang, Z., Li, R., Zhao, X., Xu, T., Zhou, T., and Hu, H. (2024). A comparative study of stereo-dependent SSVEP targets and their impact on VR-BCI performance. Front. Neurosci., 18.","DOI":"10.3389\/fnins.2024.1367932"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Mannan, M.M.N., Kamran, M.A., Kang, S., Choi, H.S., and Jeong, M.Y. (2020). A hybrid speller design using eye tracking and SSVEP brain\u2013computer interface. Sensors, 20.","DOI":"10.3390\/s20030891"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"109502","DOI":"10.1016\/j.jneumeth.2022.109502","article-title":"A classification algorithm of an SSVEP brain-Computer interface based on CCA fusion wavelet coefficients","volume":"371","author":"Ma","year":"2022","journal-title":"J. Neurosci. Methods"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1905","DOI":"10.1177\/09287329241302740","article-title":"Artificial intelligence based BCI using SSVEP signals with single channel EEG","volume":"33","author":"Kanagaluru","year":"2025","journal-title":"Technol. Health Care"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/s11571-022-09923-x","article-title":"Multi-information improves the performance of CCA-based SSVEP classification","volume":"18","author":"Yin","year":"2024","journal-title":"Cogn. Neurodyn."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Ko\u0142odziej, M., Majkowski, A., and Wiszniewski, P. (2026). Improved SSVEP Classification Through EEG Artifact Reduction Using Auxiliary Sensors. Sensors, 26.","DOI":"10.3390\/s26030917"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"144","DOI":"10.3109\/17483107.2013.785036","article-title":"A plug-and-play brain-computer interface to operate commercial assistive technology","volume":"9","author":"Thompson","year":"2014","journal-title":"Disabil. Rehabil. Assist. Technol."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Abu-Rmileh, A., Zakkay, E., Shmuelof, L., and Shriki, O. (2019). Co-adaptive training improves efficacy of a multi-day EEG-based motor imagery BCI training. Front. Hum. Neurosci., 13.","DOI":"10.3389\/fnhum.2019.00362"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/7\/2265\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T08:15:20Z","timestamp":1775549720000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/7\/2265"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,7]]},"references-count":58,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["s26072265"],"URL":"https:\/\/doi.org\/10.3390\/s26072265","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,7]]}}}