{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T13:57:25Z","timestamp":1762351045165,"version":"build-2065373602"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T00:00:00Z","timestamp":1762300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T00:00:00Z","timestamp":1762300800000},"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":["RS-2024-00361688"],"award-info":[{"award-number":["RS-2024-00361688"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Institute of Information & communications Technology Planning & Evaluation"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Data"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The inherent non-stationarity of electroencephalography (EEG) signals necessitates large, consistent datasets for reliable brain\u2013computer interface (BCI) research. In steady-state visual evoked potential (SSVEP) paradigms, prolonged exposure to visual stimuli can induce visual fatigue, leading to alterations in EEG patterns that degrade BCI performance. To mitigate fatigue-induced variability, this study employs visual stimulation in the beta frequency range (14\u201322\u2009Hz), a range that appears less susceptible to the effects of fatigue. We present a comprehensive 40-class SSVEP speller dataset acquired from 40 participants, with EEG data recorded from 31 central-to-occipital channels. Each subject completed six sessions of the SSVEP speller task in addition to pre- and post-experiment resting-state recordings under both eyes-open and eyes-closed conditions. Subjective fatigue ratings combined with EEG band power analyses confirm that beta-range stimulation minimizes fatigue effects. Moreover, the high classification accuracy achieved by calibration-based algorithms indicates that the dataset is well-suited for training advanced SSVEP-based BCI systems.<\/jats:p>","DOI":"10.1038\/s41597-025-06032-2","type":"journal-article","created":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T13:54:30Z","timestamp":1762350870000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A 40-Class SSVEP Speller Dataset: Beta Range Stimulation for Low-Fatigue BCI Applications"],"prefix":"10.1038","volume":"12","author":[{"given":"Heegyu","family":"Kim","sequence":"first","affiliation":[]},{"given":"Kyungho","family":"Won","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6512-0167","authenticated-orcid":false,"given":"Minkyu","family":"Ahn","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5357-4436","authenticated-orcid":false,"given":"Sung Chan","family":"Jun","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,5]]},"reference":[{"key":"6032_CR1","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1145\/1941487.1941506","volume":"54","author":"DJ McFarland","year":"2011","unstructured":"McFarland, D. J. & Wolpaw, J. R. Brain-computer interfaces for communication and control. Commun. ACM 54, 60\u201366 (2011).","journal-title":"Commun. ACM"},{"key":"6032_CR2","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1016\/j.tins.2006.07.004","volume":"29","author":"MA Lebedev","year":"2006","unstructured":"Lebedev, M. A. & Nicolelis, M. A. Brain\u2013machine interfaces: past, present and future. TRENDS Neurosci. 29, 536\u2013546 (2006).","journal-title":"TRENDS Neurosci."},{"key":"6032_CR3","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1109\/TRE.2000.847807","volume":"8","author":"JR Wolpaw","year":"2000","unstructured":"Wolpaw, J. R. et al. Brain-computer interface technology: a review of the first international meeting. IEEE Trans. Rehabil. Eng. 8, 164\u2013173 (2000).","journal-title":"IEEE Trans. Rehabil. Eng."},{"key":"6032_CR4","doi-asserted-by":"publisher","first-page":"139","DOI":"10.5626\/JCSE.2013.7.2.139","volume":"7","author":"KK Ang","year":"2013","unstructured":"Ang, K. K. & Guan, C. Brain-Computer Interface in Stroke Rehabilitation. J. Comput. Sci. Eng. 7, 139\u2013146 (2013).","journal-title":"J. Comput. Sci. Eng."},{"key":"6032_CR5","doi-asserted-by":"publisher","first-page":"1211","DOI":"10.3390\/s120201211","volume":"12","author":"LF Nicolas-Alonso","year":"2012","unstructured":"Nicolas-Alonso, L. F. & Gomez-Gil, J. Brain Computer Interfaces, a Review. Sensors 12, 1211\u20131279 (2012).","journal-title":"Sensors"},{"key":"6032_CR6","doi-asserted-by":"publisher","first-page":"1436","DOI":"10.1109\/TBME.2014.2300164","volume":"61","author":"S Gao","year":"2014","unstructured":"Gao, S., Wang, Y., Gao, X. & Hong, B. Visual and auditory brain\u2013computer interfaces. IEEE Trans. Biomed. Eng. 61, 1436\u20131447 (2014).","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"6032_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2022.119133","volume":"254","author":"L Kritzman","year":"2022","unstructured":"Kritzman, L. et al. Steady-state visual evoked potentials differentiate between internally and externally directed attention. NeuroImage 254, 119133 (2022).","journal-title":"NeuroImage"},{"key":"6032_CR8","doi-asserted-by":"publisher","first-page":"16683","DOI":"10.1007\/s00521-024-10143-z","volume":"36","author":"ZT Al-Qaysi","year":"2024","unstructured":"Al-Qaysi, Z. T. et al. A comprehensive review of deep learning power in steady-state visual evoked potentials. Neural Comput. Appl. 36, 16683\u201316706 (2024).","journal-title":"Neural Comput. Appl."},{"key":"6032_CR9","doi-asserted-by":"publisher","first-page":"051001","DOI":"10.1088\/1741-2552\/aaca6e","volume":"15","author":"R Zerafa","year":"2018","unstructured":"Zerafa, R., Camilleri, T., Falzon, O. & Camilleri, K. P. To train or not to train? A survey on training of feature extraction methods for SSVEP-based BCIs. J. Neural Eng. 15, 051001 (2018).","journal-title":"J. Neural Eng."},{"key":"6032_CR10","first-page":"10559","volume":"40","author":"J Hong","year":"2021","unstructured":"Hong, J. & Qin, X. Signal processing algorithms for SSVEP-based brain computer interface: State-of-the-art and recent developments. J. Intell. Fuzzy Syst. 40, 10559\u201310573 (2021).","journal-title":"J. Intell. Fuzzy Syst."},{"key":"6032_CR11","doi-asserted-by":"publisher","first-page":"1746","DOI":"10.1109\/TNSRE.2016.2627556","volume":"25","author":"Y Wang","year":"2017","unstructured":"Wang, Y., Chen, X., Gao, X. & Gao, S. A Benchmark Dataset for SSVEP-Based Brain\u2013Computer Interfaces. IEEE Trans. Neural Syst. Rehabil. Eng. 25, 1746\u20131752 (2017).","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"6032_CR12","doi-asserted-by":"crossref","unstructured":"Liu, B., Huang, X., Wang, Y., Chen, X. & Gao, X. BETA: A Large Benchmark Database Toward SSVEP-BCI Application. Front. Neurosci. 14 (2020).","DOI":"10.3389\/fnins.2020.00627"},{"key":"6032_CR13","doi-asserted-by":"publisher","DOI":"10.1093\/gigascience\/giz002","volume":"8","author":"M-H Lee","year":"2019","unstructured":"Lee, M.-H. et al. EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy. GigaScience 8, giz002 (2019).","journal-title":"GigaScience"},{"key":"6032_CR14","doi-asserted-by":"publisher","first-page":"106826","DOI":"10.1016\/j.dib.2021.106826","volume":"35","author":"G Acampora","year":"2021","unstructured":"Acampora, G., Trinchese, P. & Vitiello, A. A dataset of EEG signals from a single-channel SSVEP-based brain computer interface. Data Brief 35, 106826 (2021).","journal-title":"Data Brief"},{"key":"6032_CR15","doi-asserted-by":"publisher","first-page":"1256","DOI":"10.3390\/s21041256","volume":"21","author":"F Zhu","year":"2021","unstructured":"Zhu, F., Jiang, L., Dong, G., Gao, X. & Wang, Y. An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces. Sensors 21, 1256 (2021).","journal-title":"Sensors"},{"key":"6032_CR16","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.1109\/TNSRE.2024.3372594","volume":"32","author":"M Gu","year":"2024","unstructured":"Gu, M., Pei, W., Gao, X. & Wang, Y. Optimizing Visual Stimulation Paradigms for User-Friendly SSVEP-Based BCIs. IEEE Trans. Neural Syst. Rehabil. Eng. 32, 1090\u20131099 (2024).","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"6032_CR17","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.neucom.2016.08.121","volume":"250","author":"I Volosyak","year":"2017","unstructured":"Volosyak, I., Gembler, F. & Stawicki, P. Age-related differences in SSVEP-based BCI performance. Neurocomputing 250, 57\u201364 (2017).","journal-title":"Neurocomputing"},{"key":"6032_CR18","doi-asserted-by":"crossref","unstructured":"Azadi Moghadam, M. & Maleki, A. Fatigue factors and fatigue indices in SSVEP-based brain-computer interfaces: a systematic review and meta-analysis. Front. Hum. Neurosci. 17 (2023).","DOI":"10.3389\/fnhum.2023.1248474"},{"key":"6032_CR19","doi-asserted-by":"publisher","first-page":"1475","DOI":"10.1007\/s11517-023-03000-z","volume":"62","author":"P Diez","year":"2024","unstructured":"Diez, P., Orosco, L., Garc\u00e9s Correa, A. & Carmona, L. Assessment of visual fatigue in SSVEP-based brain-computer interface: a comprehensive study. Med. Biol. Eng. Comput. 62, 1475\u20131490 (2024).","journal-title":"Med. Biol. Eng. Comput."},{"key":"6032_CR20","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1109\/TNNLS.2020.3010780","volume":"32","author":"H Zhao","year":"2021","unstructured":"Zhao, H., Zheng, Q., Ma, K., Li, H. & Zheng, Y. Deep Representation-Based Domain Adaptation for Nonstationary EEG Classification. IEEE Trans. Neural Netw. Learn. Syst. 32, 535\u2013545 (2021).","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"6032_CR21","doi-asserted-by":"publisher","first-page":"3225","DOI":"10.3390\/s21093225","volume":"21","author":"A Kamrud","year":"2021","unstructured":"Kamrud, A., Borghetti, B. & Schubert Kabban, C. The Effects of Individual Differences, Non-Stationarity, and the Importance of Data Partitioning Decisions for Training and Testing of EEG Cross-Participant Models. Sensors 21, 3225 (2021).","journal-title":"Sensors"},{"key":"6032_CR22","doi-asserted-by":"publisher","first-page":"114905","DOI":"10.1109\/ACCESS.2021.3100478","volume":"9","author":"Y Peng","year":"2021","unstructured":"Peng, Y. et al. Fatigue Detection in SSVEP-BCIs Based on Wavelet Entropy of EEG. IEEE Access 9, 114905\u2013114913 (2021).","journal-title":"IEEE Access"},{"key":"6032_CR23","doi-asserted-by":"publisher","first-page":"1380","DOI":"10.1016\/j.clinph.2013.11.016","volume":"125","author":"MH Chang","year":"2014","unstructured":"Chang, M. H., Baek, H. J., Lee, S. M. & Park, K. S. An amplitude-modulated visual stimulation for reducing eye fatigue in SSVEP-based brain\u2013computer interfaces. Clin. Neurophysiol. 125, 1380\u20131391 (2014).","journal-title":"Clin. Neurophysiol."},{"key":"6032_CR24","doi-asserted-by":"publisher","DOI":"10.1093\/gigascience\/giz133","volume":"8","author":"G-Y Choi","year":"2019","unstructured":"Choi, G.-Y., Han, C.-H., Jung, Y.-J. & Hwang, H.-J. A multi-day and multi-band dataset for a steady-state visual-evoked potential\u2013based brain-computer interface. GigaScience 8, giz133 (2019).","journal-title":"GigaScience"},{"key":"6032_CR25","doi-asserted-by":"crossref","unstructured":"Zheng, X. et al. Anti-fatigue Performance in SSVEP-Based Visual Acuity Assessment: A Comparison of Six Stimulus Paradigms. Front. Hum. Neurosci. 14 (2020).","DOI":"10.3389\/fnhum.2020.00301"},{"key":"6032_CR26","doi-asserted-by":"crossref","unstructured":"Tian, P. et al. A quantization algorithm of visual fatigue based on underdamped second order stochastic resonance for steady state visual evoked potentials. Front. Neurosci. 17 (2023).","DOI":"10.3389\/fnins.2023.1278652"},{"key":"6032_CR27","doi-asserted-by":"publisher","first-page":"e0163426","DOI":"10.1371\/journal.pone.0163426","volume":"11","author":"J Xie","year":"2016","unstructured":"Xie, J. et al. Effects of Mental Load and Fatigue on Steady-State Evoked Potential Based Brain Computer Interface Tasks: A Comparison of Periodic Flickering and Motion-Reversal Based Visual Attention. PLOS ONE 11, e0163426 (2016).","journal-title":"PLOS ONE"},{"key":"6032_CR28","doi-asserted-by":"publisher","first-page":"108200","DOI":"10.1109\/ACCESS.2019.2932503","volume":"7","author":"Y Peng","year":"2019","unstructured":"Peng, Y. et al. Fatigue evaluation using multi-scale entropy of EEG in SSVEP-based BCI. IEEE Access 7, 108200\u2013108210 (2019).","journal-title":"IEEE Access"},{"key":"6032_CR29","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1163\/156856897X00357","volume":"10","author":"DH Brainard","year":"1997","unstructured":"Brainard, D. H. The Psychophysics Toolbox. Spat. Vis. 10, 433\u2013436 (1997).","journal-title":"Spat. Vis."},{"key":"6032_CR30","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1162\/pres.19.1.35","volume":"19","author":"Y Renard","year":"2010","unstructured":"Renard, Y. et al. OpenViBE: An Open-Source Software Platform to Design, Test, and Use Brain\u2013Computer Interfaces in Real and Virtual Environments. Presence 19, 35\u201353 (2010).","journal-title":"Presence"},{"key":"6032_CR31","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1016\/0013-4694(87)90206-9","volume":"66","author":"RW Homan","year":"1987","unstructured":"Homan, R. W., Herman, J. & Purdy, P. Cerebral location of international 10\u201320 system electrode placement. Electroencephalogr. Clin. Neurophysiol. 66, 376\u2013382 (1987).","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"6032_CR32","doi-asserted-by":"publisher","DOI":"10.6084\/m9.figshare.28806815.v2","author":"H Kim","year":"2025","unstructured":"Kim, H. et al. A 40-class SSVEP speller dataset: Beta range stimulation for low-fatigue BCI applications. figshare https:\/\/doi.org\/10.6084\/m9.figshare.28806815.v2 (2025)."},{"key":"6032_CR33","doi-asserted-by":"publisher","first-page":"2610","DOI":"10.1109\/TBME.2006.886577","volume":"53","author":"Z Lin","year":"2006","unstructured":"Lin, Z., Zhang, C., Wu, W. & Gao, X. Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs. IEEE Trans. Biomed. Eng. 53, 2610\u20132614 (2006).","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"6032_CR34","doi-asserted-by":"publisher","first-page":"1450019","DOI":"10.1142\/S0129065714500191","volume":"24","author":"M Nakanishi","year":"2014","unstructured":"Nakanishi, M., Wang, Y., Wang, Y.-T., Mitsukura, Y. & Jung, T.-P. A high-speed brain speller using steady-state visual evoked potentials. Int. J. Neural Syst. 24, 1450019 (2014).","journal-title":"Int. J. Neural Syst."},{"key":"6032_CR35","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1109\/TBME.2017.2694818","volume":"65","author":"M Nakanishi","year":"2017","unstructured":"Nakanishi, M. et al. Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis. IEEE Trans. Biomed. Eng. 65, 104\u2013112 (2017).","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"6032_CR36","doi-asserted-by":"publisher","first-page":"056013","DOI":"10.1088\/1741-2552\/aace8c","volume":"15","author":"VJ Lawhern","year":"2018","unstructured":"Lawhern, V. J. et al. EEGNet: a compact convolutional neural network for EEG-based brain\u2013computer interfaces. J. Neural Eng. 15, 056013 (2018).","journal-title":"J. Neural Eng."},{"key":"6032_CR37","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1109\/86.712231","volume":"6","author":"JR Wolpaw","year":"1998","unstructured":"Wolpaw, J. R., Ramoser, H., McFarland, D. J. & Pfurtscheller, G. EEG-based communication: improved accuracy by response verification. IEEE Trans. Rehabil. Eng. 6, 326\u2013333 (1998).","journal-title":"IEEE Trans. Rehabil. Eng."}],"container-title":["Scientific Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41597-025-06032-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41597-025-06032-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41597-025-06032-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T13:54:32Z","timestamp":1762350872000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41597-025-06032-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,5]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["6032"],"URL":"https:\/\/doi.org\/10.1038\/s41597-025-06032-2","relation":{"references":[{"id-type":"doi","id":"10.6084\/m9.figshare.28806815.v2","asserted-by":"subject"}]},"ISSN":["2052-4463"],"issn-type":[{"value":"2052-4463","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,5]]},"assertion":[{"value":"28 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1751"}}