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In this study we investigated how the outcomes of research change in response to sample size reduction. Three indices computed during a task involving the observations of four videos were considered in the analysis, two related to the brain electroencephalographic (EEG) activity and one to autonomic physiological measures, i.e., heart rate and skin conductance. The modifications of these indices were investigated considering five subgroups of sample size (32, 28, 24, 20, 16), each subgroup consisting of 630 different combinations made by bootstrapping n (n = sample size) out of 36 subjects, with respect to the total population (i.e., 36 subjects). The correlation analysis, the mean squared error (MSE), and the standard deviation (STD) of the indexes were studied at the participant reduction and three factors of influence were considered in the analysis: the type of index, the task, and its duration (time length). The findings showed a significant decrease of the correlation associated to the participant reduction as well as a significant increase of MSE and STD (p &lt; 0.05). A threshold of subjects for which the outcomes remained significant and comparable was pointed out. The effects were to some extents sensitive to all the investigated variables, but the main effect was due to the task length. Therefore, the minimum threshold of subjects for which the outcomes were comparable increased at the reduction of the spot duration.<\/jats:p>","DOI":"10.3390\/s21186088","type":"journal-article","created":{"date-parts":[[2021,9,12]],"date-time":"2021-09-12T21:48:01Z","timestamp":1631483281000},"page":"6088","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["The Sample Size Matters: To What Extent the Participant Reduction Affects the Outcomes of a Neuroscientific Research. A Case-Study in Neuromarketing Field"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8584-5023","authenticated-orcid":false,"given":"Alessia","family":"Vozzi","sequence":"first","affiliation":[{"name":"Department of Anatomical, Histological, Forensic & Orthopedic Sciences, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy"},{"name":"BrainSigns srl, Via Lungotevere Michelangelo, 9, 00192 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7174-6331","authenticated-orcid":false,"given":"Vincenzo","family":"Ronca","sequence":"additional","affiliation":[{"name":"Department of Anatomical, Histological, Forensic & Orthopedic Sciences, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy"},{"name":"BrainSigns srl, Via Lungotevere Michelangelo, 9, 00192 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3831-6620","authenticated-orcid":false,"given":"Pietro","family":"Aric\u00f2","sequence":"additional","affiliation":[{"name":"BrainSigns srl, Via Lungotevere Michelangelo, 9, 00192 Rome, Italy"},{"name":"Department of Molecular Medicine, Sapienza University of Rome, Viale Regina Elena, 291, 00161 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8560-5671","authenticated-orcid":false,"given":"Gianluca","family":"Borghini","sequence":"additional","affiliation":[{"name":"BrainSigns srl, Via Lungotevere Michelangelo, 9, 00192 Rome, Italy"},{"name":"Department of Molecular Medicine, Sapienza University of Rome, Viale Regina Elena, 291, 00161 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicolina","family":"Sciaraffa","sequence":"additional","affiliation":[{"name":"BrainSigns srl, Via Lungotevere Michelangelo, 9, 00192 Rome, Italy"},{"name":"Department of Molecular Medicine, Sapienza University of Rome, Viale Regina Elena, 291, 00161 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrizia","family":"Cherubino","sequence":"additional","affiliation":[{"name":"BrainSigns srl, Via Lungotevere Michelangelo, 9, 00192 Rome, Italy"},{"name":"Department of Molecular Medicine, Sapienza University of Rome, Viale Regina Elena, 291, 00161 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arianna","family":"Trettel","sequence":"additional","affiliation":[{"name":"BrainSigns srl, Via Lungotevere Michelangelo, 9, 00192 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4962-176X","authenticated-orcid":false,"given":"Fabio","family":"Babiloni","sequence":"additional","affiliation":[{"name":"BrainSigns srl, Via Lungotevere Michelangelo, 9, 00192 Rome, Italy"},{"name":"Department of Molecular Medicine, Sapienza University of Rome, Viale Regina Elena, 291, 00161 Rome, Italy"},{"name":"Department of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4426-051X","authenticated-orcid":false,"given":"Gianluca","family":"Di Flumeri","sequence":"additional","affiliation":[{"name":"BrainSigns srl, Via Lungotevere Michelangelo, 9, 00192 Rome, Italy"},{"name":"Department of Molecular Medicine, Sapienza University of Rome, Viale Regina Elena, 291, 00161 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.ijpsycho.2016.06.015","article-title":"Sample size calculations in human electrophysiology (EEG and ERP) studies: A systematic review and recommendations for increased rigor","volume":"111","author":"Larson","year":"2017","journal-title":"Int. J. Psychophysiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1038\/nrn3475","article-title":"Power failure: Why small sample size undermines the reliability of neuroscience","volume":"14","author":"Button","year":"2013","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1148\/radiol.2272012051","article-title":"Sample size estimation: How many individuals should be studied?","volume":"227","author":"Eng","year":"2003","journal-title":"Radiology"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sanders, N., Choo, S., and Nam, C.S. (2020). The EEG cookbook: A practical guide to neuroergonomics research. Cognitive Science and Technology, Springer.","DOI":"10.1007\/978-3-030-34784-0_3"},{"key":"ref_5","first-page":"57","article-title":"Predicting EEG sample size required for classification calibration","volume":"Volume 9743","author":"Mao","year":"2016","journal-title":"Proceedings of the Foundations of Augmented Cognition: Neuroergonomics and Operational Neuroscience\u2014AC 2016"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.1177\/0956797617723724","article-title":"Sample-size planning for more accurate statistical power: A method adjusting sample effect sizes for publication bias and uncertainty","volume":"28","author":"Anderson","year":"2017","journal-title":"Psychol. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1080\/13645579.2018.1454643","article-title":"Can sample size in qualitative research be determined a priori?","volume":"21","author":"Sim","year":"2018","journal-title":"Int. J. Soc. Res. Methodol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2593","DOI":"10.1214\/17-AOS1630","article-title":"Debiasing the lasso: Optimal sample size for Gaussian designs","volume":"46","author":"Javanmard","year":"2018","journal-title":"Ann. Stat."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.jneumeth.2015.01.010","article-title":"Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy","volume":"250","author":"Combrisson","year":"2015","journal-title":"J. Neurosci. Methods"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1037\/met0000337","article-title":"Power contours: Optimising sample size and precision in experimental psychology and human neuroscience","volume":"26","author":"Baker","year":"2021","journal-title":"Psychol. Methods"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Guttmann-Flury, E., Sheng, X., Zhang, D., and Zhu, X. (2019, January 23\u201327). A priori sample size determination for the number of subjects in an EEG experiment. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8857482"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1177\/1745691612459058","article-title":"Scientific Utopia: II. Restructuring incentives and practices to promote truth over publishability","volume":"7","author":"Nosek","year":"2012","journal-title":"Perspect. Psychol. Sci."},{"key":"ref_13","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":"Arico","year":"2018","journal-title":"Physiol. Meas."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Di Flumeri, G., Aric\u00f2, P., Borghini, G., Sciaraffa, N., Di Florio, A., and Babiloni, F. (2019). The dry revolution: Evaluation of three different eeg dry electrode types in terms of signal spectral features, mental states classification and usability. Sensors, 19.","DOI":"10.3390\/s19061365"},{"key":"ref_15","unstructured":"Ayaz, H., and Dehais, F. (2018). Neuroergonomics: The Brain at Work and in Everyday Life, Elsevier."},{"key":"ref_16","first-page":"114","article-title":"EEG-based workload estimation across affective contexts","volume":"8","author":"Jeunet","year":"2014","journal-title":"Front. Neurosci."},{"key":"ref_17","first-page":"B231","article-title":"EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks","volume":"78","author":"Berka","year":"2007","journal-title":"Aviat. Space Environ. Med."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"296","DOI":"10.3389\/fnhum.2019.00296","article-title":"Brain\u2013computer interface-based adaptive automation to prevent out-of-the-loop phenomenon in air traffic controllers dealing with highly automated systems","volume":"13","author":"Berberian","year":"2019","journal-title":"Front. Hum. Neurosci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Karthaus, M., Wascher, E., and Getzmann, S. (2018). Proactive vs. reactive car driving: EEG evidence for different driving strategies of older drivers. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0191500"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"509","DOI":"10.3389\/fnhum.2018.00509","article-title":"EEG-based mental workload neurometric to evaluate the impact of different traffic and road conditions in real driving settings","volume":"12","author":"Borghini","year":"2018","journal-title":"Front. Hum. Neurosci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Islam, M.R., Barua, S., Ahmed, M.U., Begum, S., Aric\u00f2, P., Borghini, G., and Di Flumeri, G. (2020). A novel mutual information based feature set for drivers\u2019 mental workload evaluation using machine learning. Brain Sci., 10.","DOI":"10.3390\/brainsci10080551"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"110135","DOI":"10.1016\/j.commatsci.2020.110135","article-title":"Monte Carlo simulation of order-disorder transition in refractory high entropy alloys: A data-driven approach","volume":"187","author":"Liu","year":"2021","journal-title":"Comput. Mater. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"066028","DOI":"10.1088\/1741-2560\/12\/6\/066028","article-title":"EEG-based decoding of error-related brain activity in a real-world driving task","volume":"12","author":"Chavarriaga","year":"2015","journal-title":"J. Neural Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4831","DOI":"10.1038\/s41598-021-84196-8","article-title":"The impact of multisensory integration and perceptual load in virtual reality settings on performance, workload and presence","volume":"11","author":"Marucci","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1976847","DOI":"10.1155\/2019\/1976847","article-title":"Consumer behaviour through the eyes of neurophysiological measures: State-of-the-art and future trends","volume":"2019","author":"Cherubino","year":"2019","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s12115-010-9408-1","article-title":"Neuromarketing: The new science of consumer behavior","volume":"48","author":"Morin","year":"2011","journal-title":"Society"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sciaraffa, N., Borghini, G., Di Flumeri, G., Cincotti, F., Babiloni, F., and Aric\u00f2, P. (2021). Joint analysis of eye blinks and brain activity to investigate attentional demand during a visual search task. Brain Sci., 11.","DOI":"10.3390\/brainsci11050562"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Amores, J., Richer, R., Zhao, N., Maes, P., and Eskofier, B.M. (2018, January 4\u20137). Promoting relaxation using virtual reality, olfactory interfaces and wearable EEG. Proceedings of the 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN 2018), Las Vegas, NV, USA.","DOI":"10.1109\/BSN.2018.8329668"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Di Flumeri, G., Aric\u00f2, P., Borghini, G., Sciaraffa, N., Maglione, A.G., Rossi, D., Modica, E., Trettel, A., Babiloni, F., and Colosimo, A. (2017, January 11\u201315). EEG-based Approach-Withdrawal index for the pleasantness evaluation during taste experience in realistic settings. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Jeju, Korea.","DOI":"10.1109\/EMBC.2017.8037544"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"227","DOI":"10.5057\/ijae.13.227","article-title":"Effect of smelling green tea rich in aroma components on EEG activity and memory task performance","volume":"13","author":"Yoto","year":"2014","journal-title":"Int. J. Affect. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1111\/tmi.13383","article-title":"The COVID-19 epidemic","volume":"25","author":"Velavan","year":"2020","journal-title":"Trop. Med. Int. Health"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"594566","DOI":"10.3389\/fnins.2020.594566","article-title":"Is EEG suitable for marketing research? A systematic review","volume":"14","author":"Bazzani","year":"2020","journal-title":"Front. Neurosci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1300","DOI":"10.1016\/j.neuroimage.2012.04.018","article-title":"Ten ironic rules for non-statistical reviewers","volume":"61","author":"Friston","year":"2012","journal-title":"Neuroimage"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1007\/s11517-011-0747-x","article-title":"Spectral EEG frontal asymmetries correlate with the experienced pleasantness of TV commercial advertisements","volume":"49","author":"Vecchiato","year":"2011","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3795325","DOI":"10.1155\/2016\/3795325","article-title":"Gender and age related effects while watching TV advertisements: An EEG study","volume":"2016","author":"Cartocci","year":"2016","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_36","unstructured":"Nielsen (2021, August 27). Advertising and Audiences: Making Ad Dollars Make Sense. Available online: https:\/\/www.nielsen.com\/us\/en\/insights\/article\/2014\/advertising-and-audiences-making-ad-dollars-make-sense\/."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1109\/78.554307","article-title":"A blind source separation technique using second-order statistics","volume":"45","author":"Belouchrani","year":"1997","journal-title":"IEEE Trans. Signal. Process."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.jneumeth.2003.10.009","article-title":"EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis","volume":"134","author":"Delorme","year":"2004","journal-title":"J. Neurosci. Methods"},{"key":"ref_39","unstructured":"EEGLAB Wiki (2021, May 28). 6. Reject Artifacts. Available online: https:\/\/eeglab.org\/tutorials\/06_RejectArtifacts\/."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s004220050457","article-title":"Individual differences in brain dynamics: Important implications for the calculation of event-related band power","volume":"79","author":"Doppelmayr","year":"1998","journal-title":"Biol. Cybern."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/S0165-0173(98)00056-3","article-title":"EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis","volume":"29","author":"Klimesch","year":"1999","journal-title":"Brain Res. Rev."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/BF01128870","article-title":"Global field power and topographic similarity","volume":"3","author":"Skrandies","year":"1990","journal-title":"Brain Topogr."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Society for Psychophysiological Research Ad Hoc Committee on Electrodermal Measures, Boucsein, W., Fowles, D.C., Grimnes, S., Ben-Shakhar, G., Roth, W.T., Dawson, M.E., and Filion, D.L. (2012). Publication recommendations for electrodermal measurements. Psychophysiology, 49, 1017\u20131034.","DOI":"10.1111\/j.1469-8986.2012.01384.x"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/TBME.1985.325532","article-title":"A real-time QRS detection algorithm","volume":"BME-32","author":"Pan","year":"1985","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.jneumeth.2010.04.028","article-title":"A continuous measure of phasic electrodermal activity","volume":"190","author":"Benedek","year":"2010","journal-title":"J. Neurosci. Methods"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1016\/j.tics.2012.10.007","article-title":"Alpha-band oscillations, attention, and controlled access to stored information","volume":"16","author":"Klimesch","year":"2012","journal-title":"Trends Cogn. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"7051079","DOI":"10.1155\/2019\/7051079","article-title":"EEG alpha power is modulated by attentional changes during cognitive tasks and virtual reality immersion","volume":"2019","author":"Magosso","year":"2019","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"261","DOI":"10.4236\/psych.2013.43A039","article-title":"Approach the good, withdraw from the bad\u2014A review on frontal alpha asymmetry measures in applied psychological research","volume":"4","author":"Briesemeister","year":"2013","journal-title":"Psychology"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"9616301","DOI":"10.1155\/2018\/9616301","article-title":"Neurophysiological responses to different product experiences","volume":"2018","author":"Modica","year":"2018","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"7348795","DOI":"10.1155\/2019\/7348795","article-title":"Antismoking campaigns\u2019 perception and gender differences: A comparison among EEG indices","volume":"2019","author":"Cartocci","year":"2019","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1037\/0022-3514.58.2.330","article-title":"Approach-withdrawal and cerebral asymmetry: Emotional expression and brain physiology I","volume":"58","author":"Davidson","year":"1990","journal-title":"J. Pers. Soc. Psychol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"912981","DOI":"10.1155\/2014\/912981","article-title":"Neurophysiological tools to investigate consumer\u2019s gender differences during the observation of TV commercials","volume":"2014","author":"Vecchiato","year":"2014","journal-title":"Comput. Math. Methods Med."},{"key":"ref_53","unstructured":"Bonferroni, C.E. (2021, August 24). Teoria Statistica delle Classi e Calcolo delle Probabilit\u00e0\u2014Google Libri. Available online: https:\/\/books.google.it\/books\/about\/Teoria_statistica_delle_classi_e_calcolo.html?id=3CY-HQAACAAJ&redir_esc=y."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1093\/biomet\/52.3-4.591","article-title":"An analysis of variance test for normality (complete samples)","volume":"52","author":"Shapiro","year":"1965","journal-title":"Biometrika"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1080\/01621459.1937.10503522","article-title":"The use of ranks to avoid the assumption of normality implicit in the analysis of variance","volume":"32","author":"Friedman","year":"1937","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_56","unstructured":"Nemenyi, P.B. (1963). Distribution-Free Multiple Comparisons, ProQuest."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.chb.2017.12.037","article-title":"Review on portable EEG technology in educational research","volume":"81","author":"Xu","year":"2018","journal-title":"Comput. Hum. Behav."},{"key":"ref_58","unstructured":"Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences, Routledge."},{"key":"ref_59","first-page":"69","article-title":"Statistics corner: A guide to appropriate use of correlation coefficient in medical research","volume":"24","author":"Mukaka","year":"2012","journal-title":"Malawi Med. J."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Vabalas, A., Gowen, E., Poliakoff, E., and Casson, A.J. (2019). Machine learning algorithm validation with a limited sample size. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0224365"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1975","DOI":"10.1016\/j.clinph.2013.04.010","article-title":"A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder","volume":"124","author":"Reilly","year":"2013","journal-title":"Clin. Neurophysiol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"78","DOI":"10.3389\/fnhum.2017.00078","article-title":"Evaluation of a dry EEG system for application of passive brain-computer interfaces in autonomous driving","volume":"11","author":"Zander","year":"2017","journal-title":"Front. Hum. Neurosci."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.trf.2010.06.006","article-title":"EEG signal analysis for the assessment and quantification of driver\u2019s fatigue","volume":"13","author":"Kar","year":"2010","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.bbe.2014.03.004","article-title":"Selection of an efficient feature space for EEG-based mental task discrimination","volume":"34","author":"Noshadi","year":"2014","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1016\/j.concog.2012.06.009","article-title":"Psychophysics of EEG alpha state discrimination","volume":"21","author":"Frederick","year":"2012","journal-title":"Conscious. Cogn."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1037\/a0019749","article-title":"A review of EEG, ERP, and neuroimaging studies of creativity and insight","volume":"136","author":"Dietrich","year":"2010","journal-title":"Psychol. Bull."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/18\/6088\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:00:46Z","timestamp":1760166046000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/18\/6088"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,10]]},"references-count":66,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["s21186088"],"URL":"https:\/\/doi.org\/10.3390\/s21186088","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,10]]}}}