{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:57:23Z","timestamp":1760162243201,"version":"build-2065373602"},"reference-count":60,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,10,29]],"date-time":"2023-10-29T00:00:00Z","timestamp":1698537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Brain\u2013computer interfaces (BCIs) based on steady-state visually evoked potentials (SSVEPs) are inexpensive and do not require user training. However, the highly personalized reaction to visual stimulation is an obstacle to the wider application of this technique, as it can be ineffective, tiring, or even harmful at certain frequencies. In our experimental study, we proposed a new approach to the selection of optimal frequencies of photostimulation. By using a custom photostimulation device, we covered a frequency range from 5 to 25 Hz with 1 Hz increments, recording the subjects\u2019 brainwave activity (EEG) and analyzing the signal-to-noise ratio (SNR) changes at the corresponding frequencies. The proposed set of SNR-based coefficients and the discomfort index, determined by the ratio of theta and beta rhythms in the EEG signal, enables the automation of obtaining the recommended stimulation frequencies for use in SSVEP-based BCIs.<\/jats:p>","DOI":"10.3390\/a16110502","type":"journal-article","created":{"date-parts":[[2023,10,29]],"date-time":"2023-10-29T05:01:08Z","timestamp":1698555668000},"page":"502","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Automating Stimulation Frequency Selection for SSVEP-Based Brain-Computer Interfaces"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2242-2649","authenticated-orcid":false,"given":"Alexey","family":"Kozin","sequence":"first","affiliation":[{"name":"Department of Data Collection and Processing Systems, Novosibirsk State Technical University, 630087 Novosibirsk, Russia"}]},{"given":"Anton","family":"Gerasimov","sequence":"additional","affiliation":[{"name":"Department of Data Collection and Processing Systems, Novosibirsk State Technical University, 630087 Novosibirsk, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1889-0692","authenticated-orcid":false,"given":"Maxim","family":"Bakaev","sequence":"additional","affiliation":[{"name":"Department of Data Collection and Processing Systems, Novosibirsk State Technical University, 630087 Novosibirsk, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2403-3136","authenticated-orcid":false,"given":"Anton","family":"Pashkov","sequence":"additional","affiliation":[{"name":"Department of Data Collection and Processing Systems, Novosibirsk State Technical University, 630087 Novosibirsk, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7831-9404","authenticated-orcid":false,"given":"Olga","family":"Razumnikova","sequence":"additional","affiliation":[{"name":"Department of Data Collection and Processing Systems, Novosibirsk State Technical University, 630087 Novosibirsk, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109736","DOI":"10.1016\/j.jneumeth.2022.109736","article-title":"A review of critical challenges in MI-BCI: From conventional to deep learning methods","volume":"383","author":"Khademi","year":"2023","journal-title":"J. Neurosci. Methods"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zgallai, W., Brown, J.T., Ibrahim, A., Mahmood, F., Mohammad, K., Khalfan, M., Mohammed, M., Salem, M., and Hamood, N. (April, January 26). Deep learning AI application to an EEG driven BCI smart wheelchair. Proceedings of the 2019 Advances in Science and Engineering Technology International Conferences (ASET), Dubai, United Arab Emirates.","DOI":"10.1109\/ICASET.2019.8714373"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Dehais, F., Dupres, A., Di Flumeri, G., Verdiere, K., Borghini, G., Babiloni, F., and Roy, R. (2018, January 7\u201310). Monitoring pilot\u2019s cognitive fatigue with engagement features in simulated and actual flight conditions using an hybrid fNIRS-EEG passive BCI. Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan.","DOI":"10.1109\/SMC.2018.00102"},{"key":"ref_4","unstructured":"Zander, T.O., Kothe, C., Jatzev, S., and Gaertner, M. (2010). Brain-Computer Interfaces: Applying our Minds to Human-Computer Interaction, Springer."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"011001","DOI":"10.1088\/1741-2552\/aaf12e","article-title":"A comprehensive review of EEG-based brain\u2014Computer interface paradigms","volume":"16","author":"Abiri","year":"2019","journal-title":"J. Neural Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xiao, X., Wang, L., Xu, M., Wang, K., Jung, T.P., and Ming, D. (2023). A data expansion technique based on training and testing sample to boost the detection of SSVEPs for brain-computer interfaces. J. Neural Eng.","DOI":"10.1088\/1741-2552\/acf7f6"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"258","DOI":"10.26599\/BSA.2022.9050022","article-title":"Review of brain\u2014Computer interface based on steady-state visual evoked potential","volume":"8","author":"Liu","year":"2022","journal-title":"Brain Sci. Adv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.neuroimage.2018.04.006","article-title":"Representation of steady-state visual evoked potentials elicited by luminance flicker in human occipital cortex: An electrocorticography study","volume":"175","author":"Wittevrongel","year":"2018","journal-title":"Neuroimage"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"E6058","DOI":"10.1073\/pnas.1508080112","article-title":"High-speed spelling with a noninvasive brain\u2014Computer interface","volume":"112","author":"Chen","year":"2015","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"702357","DOI":"10.1155\/2010\/702357","article-title":"A survey of stimulation methods used in SSVEP-based BCIs","volume":"2010","author":"Zhu","year":"2010","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1109\/86.712231","article-title":"EEG-based communication: Improved accuracy by response verification","volume":"6","author":"Wolpaw","year":"1998","journal-title":"IEEE Trans. Rehabil. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1103935","DOI":"10.3389\/fnhum.2023.1103935","article-title":"An online hybrid BCI combining SSVEP and EOG-based eye movements","volume":"17","author":"Zhang","year":"2023","journal-title":"Front. Hum. Neurosci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"016014","DOI":"10.1088\/1741-2560\/13\/1\/016014","article-title":"Effect of higher frequency on the classification of steady-state visual evoked potentials","volume":"13","author":"Won","year":"2015","journal-title":"J. Neural Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Adams, M., Benda, M., Saboor, A., Krause, A.F., Rezeika, A., Gembler, F., Stawicki, P., Hesse, M., Essig, K., and Ben-Salem, S. (2019, January 6\u20139). Towards an SSVEP-BCI Controlled Smart Home. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Bari, Italy.","DOI":"10.1109\/SMC.2019.8914668"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"035002","DOI":"10.1088\/1741-2560\/11\/3\/035002","article-title":"An independent SSVEP-based brain\u2013computer interface in locked-in syndrome","volume":"11","author":"Lesenfants","year":"2014","journal-title":"J. Neural Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"103101","DOI":"10.1016\/j.dsp.2021.103101","article-title":"An embedded lightweight SSVEP-BCI electric wheelchair with hybrid stimulator","volume":"116","author":"Na","year":"2021","journal-title":"Digit. Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1521","DOI":"10.1109\/TNSRE.2023.3245654","article-title":"Representative-Based Cold Start for Adaptive SSVEP-BCI","volume":"31","author":"Shi","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Han, D.K., and Jeong, J.H. (2021, January 22\u201324). Domain generalization for session-independent brain-computer interface. Proceedings of the 2021 9th International Winter Conference on Brain-Computer Interface (BCI), Gangwon, Republic of Korea.","DOI":"10.1109\/BCI51272.2021.9385322"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.neunet.2023.03.039","article-title":"A novel semi-supervised meta learning method for subject-transfer brain\u2013computer interface","volume":"163","author":"Li","year":"2023","journal-title":"Neural Netw."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, M., and Xu, D. (2022). Transfer Learning in Motor Imagery Brain Computer Interface: A Review. J. Shanghai Jiaotong Univ. Sci., 1\u201323.","DOI":"10.1007\/s12204-022-2488-4"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"8865","DOI":"10.1038\/s41598-022-12733-0","article-title":"Improving user experience of SSVEP BCI through low amplitude depth and high frequency stimuli design","volume":"12","author":"Ladouce","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Silva, L.C.B., Kasteleijn-Nolst Trenite, D., Manreza, M.L., and Appleton, R.E. (2021). The Importance of Photosensitivity for Epilepsy, Springer.","DOI":"10.1007\/978-3-319-05080-5"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1771","DOI":"10.1109\/TNSRE.2020.3005771","article-title":"Stress-Induced Effects in Resting EEG Spectra Predict the Performance of SSVEP-Based BCI","volume":"28","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"782","DOI":"10.3758\/s13415-013-0238-7","article-title":"EEG theta\/beta ratio as a potential biomarker for attentional control and resilience against deleterious effects of stress on attention","volume":"14","author":"Putman","year":"2014","journal-title":"Cogn. Affect. Behav. Neurosci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Vanhollebeke, G., De Smet, S., De Raedt, R., Baeken, C., van Mierlo, P., and Vanderhasselt, M.A. (2022). The neural correlates of psychosocial stress: A systematic review and meta-analysis of spectral analysis EEG studies. Neurobiol. Stress, 18.","DOI":"10.1016\/j.ynstr.2022.100452"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.ijpsycho.2012.09.008","article-title":"Stress, emotion regulation and cognitive performance: The predictive contributions of trait and state relative frontal EEG alpha asymmetry","volume":"87","author":"Goodman","year":"2013","journal-title":"Int. J. Psychophysiol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1080\/00140139.2012.676675","article-title":"Brain\u2013computer interface (BCI) and ergonomics","volume":"55","author":"Nam","year":"2012","journal-title":"Ergonomics"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bos, D.P.O., Reuderink, B., van de Laar, B., G\u00fcrk\u00f6k, H., M\u00fchl, C., Poel, M., Heylen, D., and Nijholt, A. (2010, January 20\u201322). Human-computer interaction for BCI games: Usability and user experience. Proceedings of the 2010 International Conference on Cyberworlds, Singapore.","DOI":"10.1109\/CW.2010.22"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"van de Laar, B., G\u00fcrk\u00f6k, H., Plass-Oude Bos, D., Nijboer, F., and Nijholt, A. (2011, January 9\u201314). Perspectives on user experience evaluation of brain-computer interfaces. Proceedings of the Universal Access in Human-Computer Interaction, Users Diversity: 6th International Conference, UAHCI 2011, Held as Part of HCI International 2011, Orlando, FL, USA.","DOI":"10.1007\/978-3-642-21663-3_65"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1142892","DOI":"10.3389\/fnins.2023.1142892","article-title":"SSVEP detection assessment by combining visual stimuli paradigms and no-training detection methods","volume":"17","author":"Caraza","year":"2023","journal-title":"Front. Neurosci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ku\u015b, R., Duszyk, A., Milanowski, P., \u0141ab\u0119cki, M., Bierzy\u0144ska, M., Radzikowska, Z., Michalska, M., \u017bygierewicz, J., Suffczy\u0144ski, P., and Durka, P.J. (2013). On the Quantification of SSVEP Frequency Responses in Human EEG in Realistic BCI Conditions. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0077536"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"036008","DOI":"10.1088\/1741-2560\/9\/3\/036008","article-title":"Stimulus specificity of a steady-state visual-evoked potential-based brain\u2013computer interface","volume":"9","author":"Ng","year":"2012","journal-title":"J. Neural Eng."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wu, Y., Yang, R., Chen, W., Li, X., and Niu, J. (2022). Research on Unsupervised Classification Algorithm Based on SSVEP. Appl. Sci., 12.","DOI":"10.3390\/app12168274"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1707","DOI":"10.1097\/00001756-199806010-00007","article-title":"Restoration of neural output from a paralyzed patient by a direct brain connection","volume":"9","author":"Kennedy","year":"1998","journal-title":"NeuroReport"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1038\/nature04970","article-title":"Neuronal ensemble control of prosthetic devices by a human with tetraplegia","volume":"442","author":"Hochberg","year":"2006","journal-title":"Nature"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1038\/s42256-023-00714-5","article-title":"Decoding speech perception from non-invasive brain recordings","volume":"5","author":"Caucheteux","year":"2023","journal-title":"Nat. Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Orban, M., Elsamanty, M., Guo, K., Zhang, S., and Yang, H. (2022). A Review of Brain Activity and EEG-Based Brain\u2013Computer Interfaces for Rehabilitation Application. Bioengineering, 9.","DOI":"10.3390\/bioengineering9120768"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"102006","DOI":"10.1016\/j.inffus.2023.102006","article-title":"Finger pinching and imagination classification: A fusion of CNN architectures for IoMT-enabled BCI applications","volume":"101","author":"Varone","year":"2024","journal-title":"Inf. Fusion"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"08TR02","DOI":"10.1088\/1361-6579\/aad57e","article-title":"Passive BCI beyond the lab: Current trends and future directions","volume":"39","author":"Borghini","year":"2018","journal-title":"Physiol. Meas."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"E2","DOI":"10.3171\/2020.4.FOCUS20185","article-title":"The current state of electrocorticography-based brain\u2013computer interfaces","volume":"49","author":"Miller","year":"2020","journal-title":"Neurosurg. Focus"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2018","DOI":"10.1109\/TBME.2021.3133594","article-title":"Online adaptation boosts SSVEP-based BCI performance","volume":"69","author":"Wong","year":"2021","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"051001","DOI":"10.1088\/1741-2552\/aaca6e","article-title":"To train or not to train? A survey on training of feature extraction methods for SSVEP-based BCIs","volume":"15","author":"Zerafa","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1870","DOI":"10.1109\/TNSRE.2022.3184402","article-title":"Reducing the calibration time in somatosensory BCI by using tactile ERD","volume":"30","author":"Yao","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.artmed.2013.08.001","article-title":"Brain\u2013computer interface controlled gaming: Evaluation of usability by severely motor restricted end-users","volume":"59","author":"Holz","year":"2013","journal-title":"Artif. Intell. Med."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1134\/S0361768822080163","article-title":"Usability Evaluation of BCI Software Applications: A systematic review of the literature","volume":"48","author":"Ortega","year":"2022","journal-title":"Program. Comput. Softw."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"056046","DOI":"10.1088\/1741-2552\/ac284a","article-title":"Optimizing spatial properties of a new checkerboard-like visual stimulus for user-friendly SSVEP-based BCIs","volume":"18","author":"Ming","year":"2021","journal-title":"J. Neural Eng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"016042","DOI":"10.1088\/1741-2552\/acb50e","article-title":"A high-performance SSVEP-based BCI using imperceptible flickers","volume":"20","author":"Ming","year":"2023","journal-title":"J. Neural Eng."},{"key":"ref_48","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_49","doi-asserted-by":"crossref","unstructured":"Davidov, A., Razumnikova, O., and Bakaev, M. (2023). Nature in the Heart and Mind of the Beholder: Psycho-Emotional and EEG Differences in Perception of Virtual Nature Due to Gender. Vision, 7.","DOI":"10.3390\/vision7020030"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.biopsycho.2009.10.008","article-title":"EEG theta\/beta ratio in relation to fear-modulated response-inhibition, attentional control, and affective traits","volume":"83","author":"Putman","year":"2010","journal-title":"Biol. Psychol."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Altaf, H., Ibrahim, S.N., Azmin, N., Asnawi, A.L., Walid, B.H.B., and Harun, N. (2021, January 20\u201321). Machine Learning Approach for Stress Detection based on Alpha-Beta and Theta-Beta Ratios of EEG Signals. Proceedings of the 2021 13th International Conference on Information & Communication Technology and System (ICTS), Surabaya, Indonesia.","DOI":"10.1109\/ICTS52701.2021.9608810"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Schutter, D.J.L.G., and Kenemans, J.L. (2022). Theta-Beta Power Ratio: An Electrophysiological Signature of Motivation, Attention and Cognitive Control, Oxford University Press.","DOI":"10.1093\/oxfordhb\/9780192898340.013.15"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Jiang, X., Bian, G.B., and Tian, Z. (2019). Removal of Artifacts from EEG Signals: A Review. Sensors, 19.","DOI":"10.3390\/s19050987"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1109\/TNSRE.2023.3243786","article-title":"Optimizing Stimulus Frequency Ranges for Building a High-Rate High Frequency SSVEP-BCI","volume":"31","author":"Chen","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_55","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_56","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.1109\/TNSRE.2022.3185262","article-title":"SSVEP-Based Brain Computer Interface Controlled Soft Robotic Glove for Post-Stroke Hand Function Rehabilitation","volume":"30","author":"Guo","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"355","DOI":"10.3109\/17483107.2014.961569","article-title":"Application of BCI systems in neurorehabilitation: A scoping review","volume":"10","author":"Bamdad","year":"2013","journal-title":"Disabil. Rehabil. Assist. Technol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"38","DOI":"10.3389\/fneng.2014.00038","article-title":"Challenges in clinical applications of brain computer interfaces in individuals with spinal cord injury","volume":"7","author":"Rupp","year":"2014","journal-title":"Front. Neuroeng."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1093\/brain\/awaa026","article-title":"Prognosis for patients with cognitive motor dissociation identified by brain-computer interface","volume":"143","author":"Pan","year":"2020","journal-title":"Brain"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/THMS.2020.2983661","article-title":"Performance of a Steady-State Visual Evoked Potential and Eye Gaze Hybrid Brain-Computer Interface on Participants with and without a Brain Injury","volume":"50","author":"Brennan","year":"2020","journal-title":"IEEE Trans. Hum.-Mach. 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