{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T22:49:36Z","timestamp":1770590976832,"version":"3.49.0"},"reference-count":111,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2019,6,26]],"date-time":"2019-06-26T00:00:00Z","timestamp":1561507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Industry 4.0 leaders solve problems all of the time. Successful problem-solving behavioral pattern choice determines organizational and personal success, therefore a proper understanding of the problem-solving-related neurological dynamics is sure to help increase business performance. The purpose of this paper is two-fold: first, to discover relevant neurological characteristics of problem-solving behavioral patterns, and second, to conduct a characterization of two problem-solving behavioral patterns with the aid of deep-learning architectures. This is done by combining electroencephalographic non-invasive sensors that capture process owners\u2019 brain activity signals and a deep-learning soft sensor that performs an accurate characterization of such signals with an accuracy rate of over 99% in the presented case-study dataset. As a result, the deep-learning characterization of lean management (LM) problem-solving behavioral patterns is expected to help Industry 4.0 leaders in their choice of adequate manufacturing systems and their related problem-solving methods in their future pursuit of strategic organizational goals.<\/jats:p>","DOI":"10.3390\/s19132841","type":"journal-article","created":{"date-parts":[[2019,6,26]],"date-time":"2019-06-26T07:24:17Z","timestamp":1561533857000},"page":"2841","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Characterization of Industry 4.0 Lean Management Problem-Solving Behavioral Patterns Using EEG Sensors and Deep Learning"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2423-1474","authenticated-orcid":false,"given":"Javier","family":"Villalba-Diez","sequence":"first","affiliation":[{"name":"Fakult\u00e4t Management und Vertrieb, Hochschule Heilbronn Campus Schw\u00e4bisch Hall, 74523 Schw\u00e4bisch Hall, Germany"},{"name":"Departament of Artificial Intelligence, Escuela T\u00e9cnica Superior de Ingenieros Inform\u00e1ticos, Universidad Polit\u00e9cnica de Madrid, 28660 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaochen","family":"Zheng","sequence":"additional","affiliation":[{"name":"Departament of Business Intelligence, Escuela T\u00e9cnica Superior de Ingenieros Industriales, Universidad Polit\u00e9cnica de Madrid, 2006 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8917-2041","authenticated-orcid":false,"given":"Daniel","family":"Schmidt","sequence":"additional","affiliation":[{"name":"Saueressig GmbH + Co. 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[2nd ed.]."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"6679","DOI":"10.1080\/00207540802230504","article-title":"In pursuit of implementation patterns: The context of Lean and Six Sigma","volume":"46","author":"Shah","year":"2008","journal-title":"Int. J. Prod. Econ."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Villalba-Diez, J. (2017). The HOSHIN KANRI FOREST. Lean Strategic Organizational Design, CRC Press\/Taylor and Francis Group LLC. [1st ed.].","DOI":"10.1201\/9781315155814"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Villalba-Diez, J. (2017). The Lean Brain Theory. Complex Networked Lean Strategic Organizational Design, CRC Press\/Taylor and Francis Group LLC.","DOI":"10.1201\/9781315155814-2"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/S0272-6963(02)00108-0","article-title":"Lean Manufacturing: Context, practice bundles and performance","volume":"21","author":"Shah","year":"2003","journal-title":"J. Oper. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1016\/j.jom.2007.01.019","article-title":"Defining and developing measures of lean production","volume":"25","author":"Shah","year":"2007","journal-title":"J. Oper. Manag."},{"key":"ref_8","unstructured":"Shewhart, W., and Deming, E. (1939). Statistical Method. From the Viewpoint of Quality Control, Department of Agriculture, The Graduate School."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1080\/14783363.2014.969909","article-title":"Application of just in time as a total quality management tool: The case of an aluminium foundry manufacturing","volume":"27","author":"Madanhire","year":"2016","journal-title":"Total Qual. Manag. Bus. Excell."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yang, C.C. (2018). The effectiveness analysis of the practices in five quality management stages for SMEs. Total Qual. Manag. Bus. Excell.","DOI":"10.1080\/14783363.2018.1456010"},{"key":"ref_11","unstructured":"Rother, M. (2009). Toyota Kata: Managing People for Improvement, Adaptiveness and Superior Results, McGraw-Hill. [1st ed.]."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sobek, D., and Smalley, A. (2008). Understanding A3 Thinking: A Critical Component of Toyota\u2019s PDCA Management System, Productivity Press. [1st ed.].","DOI":"10.4324\/9781439814055"},{"key":"ref_13","unstructured":"Balle, M., and Balle, F. (2014). Lead with Respect: A Novel of Lean Practice, Lean Enterprises Inst Inc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1109\/TEM.2015.2424156","article-title":"Improving manufacturing operational performance by standardizing process management","volume":"62","year":"2015","journal-title":"Trans. Eng. Manag."},{"key":"ref_15","unstructured":"Rother, M., and Aulinger, G. (2017). Toyota Kata Culture: Building Organizational Capability and Mindset through Kata Coaching, McGraw-Hill Higher Education."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.procir.2016.05.101","article-title":"Lean Learning Patterns. (CPD)nA vs. KATA","volume":"54","year":"2016","journal-title":"Procedia CIRP"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"12847","DOI":"10.3390\/s140712847","article-title":"Dry EEG Electrodes","volume":"14","author":"Valle","year":"2014","journal-title":"Sensors"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Pathirana, S., Asirvatham, D., and Johar, G. (2018, January 24\u201326). A Critical Evaluation on Low-Cost Consumer-Grade Electroencephalographic Devices. Proceedings of the 2018 2nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS), Kuching, Malaysia.","DOI":"10.1109\/ICBAPS.2018.8527413"},{"key":"ref_20","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_21","doi-asserted-by":"crossref","unstructured":"Goldberg, E. (2009). The New Executive Brain. Frontal Lobes in a Complex World, Oxford University Press.","DOI":"10.1093\/oso\/9780195329407.001.0001"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1006\/cogp.1999.0734","article-title":"The unity and diversity of executive functions and their contributions to complex \u201cFrontal Lobe\u201d tasks: A latent variable analysis","volume":"41","author":"Miyake","year":"2000","journal-title":"Cogn. Psychol."},{"key":"ref_23","unstructured":"Imai, M. (2012). Gemba Kaizen: A Commonsense Approach to a Continuous Improvement Strategy, McGraw-Hill Professional. [2nd ed.]."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.jmsy.2011.11.001","article-title":"Scalability planning for reconfigurable manufacturing systems","volume":"31","author":"Wang","year":"2012","journal-title":"J. Manuf. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1037\/edu0000116","article-title":"Working memory components and problem-solving accuracy: Are there multiple pathways?","volume":"108","author":"Swanson","year":"2016","journal-title":"J. Educ. Psychol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fuster, J. (2015). The Prefrontal Cortex, Academic Press. [5th ed.].","DOI":"10.1016\/B978-0-12-407815-4.00002-7"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1863","DOI":"10.1249\/MSS.0b013e3182172a6f","article-title":"Brain cortical activity is influenced by exercise mode and intensity","volume":"43","author":"Schneider","year":"2011","journal-title":"Med. Sci. Sports Exerc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1037\/pmu0000031","article-title":"Measuring musical engagement using expressive movement and EEG brain dynamics","volume":"24","author":"Leslie","year":"2014","journal-title":"Psychomusicol. Music Mind Brain"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1038\/nature14396","article-title":"A prefrontal\u2013thalamo\u2013hippocampal circuit for goal-directed spatial navigation","volume":"522","author":"Ito","year":"2015","journal-title":"Nature"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1126\/science.aaf0784","article-title":"Prospective representation of navigational goals in the human hippocampus","volume":"352","author":"Brown","year":"2016","journal-title":"Science"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.neuroimage.2016.11.015","article-title":"Loss of lateral prefrontal cortex control in food-directed attention and goal-directed food choice in obesity","volume":"146","author":"Janssen","year":"2017","journal-title":"NeuroImage"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/j.apmr.2010.11.015","article-title":"Evidence-Based Cognitive Rehabilitation: Updated Review of the Literature From 1998 Through 2002","volume":"92","author":"Cicerone","year":"2011","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1016\/j.tics.2016.09.007","article-title":"Oscillatory Dynamics of Prefrontal Cognitive Control","volume":"20","author":"Helfrich","year":"2016","journal-title":"Trends Cogn. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.neuron.2017.11.031","article-title":"Reward-Based Learning Drives Rapid Sensory Signals in Medial Prefrontal Cortex and Dorsal Hippocampus Necessary for Goal-Directed Behavior","volume":"97","author":"Merre","year":"2018","journal-title":"Neuron"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1093\/brain\/awx105","article-title":"Selective impairment of goal-directed decision-making following lesions to the human ventromedial prefrontal cortex","volume":"140","author":"Reber","year":"2017","journal-title":"Brain"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3353","DOI":"10.1073\/pnas.1518147113","article-title":"Behavioral response inhibition and maturation of goal representation in prefrontal cortex after puberty","volume":"113","author":"Zhou","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1318","DOI":"10.1038\/nn.4071","article-title":"Oscillatory dynamics coordinating human frontal networks in support of goal maintenance","volume":"18","author":"Voytek","year":"2015","journal-title":"Nat. Neurosci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1162\/0898929053279522","article-title":"Top-down Enhancement and Suppression of the Magnitude and Speed of Neural Activity","volume":"17","author":"Gazzaley","year":"2005","journal-title":"J. Cogn. Neurosci."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Goldman-Rakic, P. (1987). Circuitry of primate prefrontal cortex and regulation of behavior by representational memory. Comprehensive Physiology, Supplement 5: Handbookof Physiology, the Nervous System, Higher Functions of the Brain, John Wiley & Sons, Inc.","DOI":"10.1002\/cphy.cp010509"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2082","DOI":"10.1162\/jocn.2007.19.12.2082","article-title":"Functional magnetic resonance imaging evidence for a hierarchical organization of the prefrontal cortex","volume":"19","author":"Badre","year":"2007","journal-title":"J. Cogn. Neurosci."},{"key":"ref_41","first-page":"615","article-title":"Meeting ofminds: The medial frontal cortex and social cognition","volume":"88","author":"Amodio","year":"2006","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e55","DOI":"10.1016\/j.biopsych.2010.07.024","article-title":"From reactive to proactive and selective control: Developing a richer model for stopping inappropriate responses","volume":"69","author":"Aron","year":"2011","journal-title":"Biol. Psychiatry"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e00686","DOI":"10.1002\/brb3.686","article-title":"Resting state and personality component (BIS\/BAS) predict the brain activity (EEG and fNIRS measure) in response to emotional cues","volume":"7","author":"Balconi","year":"2017","journal-title":"Brain Behav."},{"key":"ref_44","first-page":"1270","article-title":"Adaptive Engagement of Cognitive Control in Context-Dependent Decision Making","volume":"27","author":"Waskom","year":"2017","journal-title":"Cereb. Cortex"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1126\/science.1168450","article-title":"Self-Control in Decision-Maiking Involves Modulation of the vmPFC Valuation System","volume":"324","author":"Hare","year":"2009","journal-title":"Science"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"15988","DOI":"10.1523\/JNEUROSCI.3192-14.2014","article-title":"Interactions between Dorsolateral and Ventromedial Prefrontal Cortex Underlie Context-Dependent Stimulus Valuation in Goal-Directed Choice","volume":"34","author":"Rudort","year":"2014","journal-title":"J. Neurosci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1605","DOI":"10.1097\/00001756-199508000-00005","article-title":"Human prefrontal lesions increase distractability to irrelevant sensory inputs","volume":"6","author":"Chao","year":"1995","journal-title":"Neuroreport"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fnsys.2015.00169","article-title":"The Effect of Disruption of Prefrontal Cortical Function with Transcranial Magnetic Stimulation on Visual Working Memory","volume":"9","author":"Lorenc","year":"2015","journal-title":"Front. Syst. Neurosci."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Spreng, R., Shoemaker, L., and Turner, G. (2017). Executive Functions and Neurocognitive Aging. Executive Functions in Health and Disease, Academic Press. [1st ed.].","DOI":"10.1016\/B978-0-12-803676-1.00008-8"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.ynstr.2014.10.002","article-title":"The effects of stress exposure on prefrontal cortex: Translating absic research into successful treatments for post-traumatic stress disorder","volume":"1","author":"Arnsten","year":"2015","journal-title":"Neurobiol. Stress"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1038\/nrn2648","article-title":"Stress signalling pathways that impair prefrontal cortex structure and function","volume":"10","author":"Arnsten","year":"2009","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1001\/archpsyc.62.7.799","article-title":"Deficient fear conditioning in psychopathy: a functional magnetic resonance imaging study","volume":"62","author":"Birbaumer","year":"2005","journal-title":"Arch. Gen. Psychiatry"},{"key":"ref_53","first-page":"647","article-title":"Functional Connectivity Bias in the Prefrontal Cortex of Psychopaths","volume":"78","author":"Pujol","year":"2014","journal-title":"Biol. Psychiatry"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1093\/scan\/nsr075","article-title":"Breakdown in the brain network subserving moral judgment in criminal psychopathy","volume":"7","author":"Pujol","year":"2012","journal-title":"Soc. Cogn. Affect. Neurosci."},{"key":"ref_55","unstructured":"Babiak, P., and Hare, R. (2007). Snakes in Suits: When Psychopaths Go to Work, HarperBusiness. [Reprint edition]."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"6415","DOI":"10.1038\/s41598-017-06662-6","article-title":"Collaborative roles of Temporoparietal Junction and Dorsolateral Prefrontal Cortex in Different Types of Behavioural Flexibility","volume":"7","author":"Tei","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"8399","DOI":"10.1523\/JNEUROSCI.0485-17.2017","article-title":"Reconfiguration of brain network architectures between resting state and complexity-dependent cognitive reasoning","volume":"37","author":"Hearne","year":"2017","journal-title":"J. Neurosci."},{"key":"ref_58","unstructured":"Rother, M., and Shook, J. (1999). Learning to See: Value Stream Mapping to Add Value and Eliminate MUDA, Lean Enterprise Insititute. [1st ed.]."},{"key":"ref_59","unstructured":"Womack, J., and Jones, D. (2011). Seeing the Whole Value Stream, Lean Enterprise Institute. [2nd ed.]."},{"key":"ref_60","first-page":"99","article-title":"Study on stable facility conservation activities based on PDCA cycle","volume":"17","author":"Baba","year":"2012","journal-title":"Yokohama Int. Soc. Sci. Res."},{"key":"ref_61","unstructured":"Center, J.M.A.M. (2013). PDCA Starting from C Works Faster!, Japan Management Association Management Center."},{"key":"ref_62","first-page":"103","article-title":"Validation and Benchmarking of a Wearable EEG Acquisition Platform for Real-World Applications","volume":"13","author":"Valentin","year":"2019","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1109\/TBCAS.2014.2316224","article-title":"Wireless and Wearable EEG System for Evaluating Driver Vigilance","volume":"8","author":"Lin","year":"2014","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Luo, D., Rasim, Y., Li, Y., Meng, G., Xu, J., and Wang, C. (2016). A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation. Sensors, 16.","DOI":"10.3390\/s16020242"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Zhang, X., Li, J., Liu, Y., Zhang, Z., Wang, Z., Luo, D., Zhou, X., Zhu, M., Salman, W., and Hu, G. (2017). Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG. Sensors, 17.","DOI":"10.3390\/s17030486"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Mohamed, Z., El Halaby, M., Said, T., Shawky, D., and Badawi, A. (2018). Characterizing Focused Attention and Working Memory Using EEG. Sensors, 18.","DOI":"10.3390\/s18113743"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Masood, N., and Farooq, H. (2019). Investigating EEG Patterns for Dual-Stimuli Induced Human Fear Emotional State. Sensors, 19.","DOI":"10.3390\/s19030522"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Ahn, J.W., Ku, Y., and Kim, H.C. (2019). A Novel Wearable EEG and ECG Recording System for Stress Assessment. Sensors, 19.","DOI":"10.3390\/s19091991"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Blanco, J.A., Vanleer, A.C., Calibo, T.K., and Firebaugh, S.L. (2019). Single-Trial Cognitive Stress Classification Using Portable Wireless Electroencephalography. Sensors, 19.","DOI":"10.3390\/s19030499"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"P\u00e9rez-Vidal, A.F., Garcia-Beltran, C.D., Mart\u00ednez-Sibaja, A., and Posada-G\u00f3mez, R. (2018). Use of the Stockwell Transform in the Detection of P300 Evoked Potentials with Low-Cost Brain Sensors. Sensors, 18.","DOI":"10.3390\/s18051483"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Shen, Y. (2019). Parallel Mechanism of Spectral Feature-Enhanced Maps in EEG-Based Cognitive Workload Classification. Sensors, 19.","DOI":"10.3390\/s19040808"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Borghini, G., Aric\u00f2, P., Di Flumeri, G., Sciaraffa, N., and Babiloni, F. (2019). Correlation and Similarity between Cerebral and Non-Cerebral Electrical Activity for User\u2019s States Assessment. Sensors, 19.","DOI":"10.3390\/s19030704"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"244","DOI":"10.3389\/fnhum.2014.00244","article-title":"Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface","volume":"8","author":"Khan","year":"2014","journal-title":"Front. Hum. Neurosci."},{"key":"ref_74","unstructured":"(1994). American Electroencephalographic Society guidelines in electroencephalography, evoked potentials, and polysomnography. J. Clin. Neurophysiol., 11, 147."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.neucom.2014.08.092","article-title":"Feature learning from incomplete EEG with denoising autoencoder","volume":"165","author":"Li","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"103370Y","DOI":"10.1117\/12.2267828","article-title":"Mathematical approach to recover EEG brain signals with artifacts by means of Gram-Schmidt transform","volume":"10337","author":"Runnova","year":"2017","journal-title":"SPIE Proc."},{"key":"ref_77","first-page":"12","article-title":"Brain Computer Interface: EEG Signal Preprocessing Issues and Solutions","volume":"169","author":"Elsayed","year":"2017","journal-title":"Int. J. Comput. Appl."},{"key":"ref_78","unstructured":"Shanbao, T., and Thankor, N. (2009). Cross-Correlation Function. Quantitative EEG Analysis Methods and Applications (Engineering in Medicine & Biology), Artech House Publishers."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1016\/j.eswa.2007.11.017","article-title":"Cross-correlation aided support vector machine classifier for classification of EEG signals","volume":"36","author":"Chandaka","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1007\/s11517-010-0696-9","article-title":"Cross-correlation of EEG frequency bands and heart rate variability for sleep apnoea classification","volume":"48","author":"Abdullah","year":"2010","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/s10527-013-9357-2","article-title":"Use of Cross-Correlation Analysis of EEG Signals for Detecting Risk Level for Development of Schizophrenia","volume":"47","author":"Panischev","year":"2013","journal-title":"Biomed. Eng."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Morelli, M., Giannoni, A., Passino, C., Landini, L., Emdin, M., and Vanello, N. (2016). A Cross-Correlational Analysis between Electroencephalographic and End-Tidal Carbon Dioxide Signals: Methodological Issues in the Presence of Missing Data and Real Data Results. Sensors, 16.","DOI":"10.3390\/s16111828"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"185","DOI":"10.17654\/EC017010185","article-title":"Signal reference selection and dimensionality reduction for crosscorrelation based feature extraction in EEG signals of brain computer interface","volume":"17","author":"Hermanto","year":"2017","journal-title":"Far East J. Electron. Commun."},{"key":"ref_84","unstructured":"Turner, J., Page, A., Mohsenin, T., and Oates, T. (2014, January 24\u201326). Deep Belief Networks used on High Resolution Multichannel Electroencephalography Data for Seizure Detection. Proceedings of the 2014 AAAI Spring Symposium, Palo Alto, CA, USA."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Jia, X., Li, K., Li, X., and Zhang, A. (2014, January 10\u201312). A novel semi-supervised deep learning framework for affective state recognition on eeg signals. Proceedings of the 2014 IEEE International Conference on Bioinformatics and Bioengineering (BIBE), Boca Raton, FL, USA.","DOI":"10.1109\/BIBE.2014.26"},{"key":"ref_86","first-page":"3","article-title":"From multilayer perceptrons and neurofuzzy systems to deep learning machines: Which method to use?\u2014A survey","volume":"9","author":"Kasabov","year":"2017","journal-title":"Int. J. Inf. Technol. Secur."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"8659","DOI":"10.1016\/j.eswa.2010.06.065","article-title":"EEG signal classification using PCA, ICA, LDA and support vector machines","volume":"37","author":"Subasi","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Chen, D.W., Miao, R., Yang, W.Q., Liang, Y., Chen, H.H., Huang, L., Deng, C.J., and Han, N. (2019). A Feature Extraction Method Based on Differential Entropy and Linear Discriminant Analysis for Emotion Recognition. Sensors, 19.","DOI":"10.3390\/s19071631"},{"key":"ref_89","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, The MIT Press. [1st ed.]."},{"key":"ref_90","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","volume":"Volume 9","author":"Glorot","year":"2010","journal-title":"Artificial Intelligence and Statistics (AISTATS)"},{"key":"ref_91","unstructured":"Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., and Ng, A.Y. (July, January 28). Multimodel deep learning. Proceedings of the 28th International Conference on Machine Learning, Bellevue, WA, USA."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000006","article-title":"Learning deep architechtures for AI","volume":"2","author":"Bengio","year":"2009","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1109\/JBHI.2017.2727218","article-title":"Deep belief networks for electroencephalography: A review of recent contributions and future outlooks","volume":"22","author":"Movahedi","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"5391","DOI":"10.1002\/hbm.23730","article-title":"Deep learning with convolutional neural networks for brain mapping and decoding of movement-related information from the human EEG","volume":"38","author":"Schirrmeister","year":"2017","journal-title":"Hum. Brain Mapp."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Dai, M., Zheng, D., Na, R., Wang, S., and Zhang, S. (2019). EEG Classification of Motor Imagery Using a Novel Deep Learning Framework. Sensors, 19.","DOI":"10.3390\/s19030551"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Majidov, I., and Whangbo, T. (2019). Efficient Classification of Motor Imagery Electroencephalography Signals Using Deep Learning Methods. Sensors, 19.","DOI":"10.3390\/s19071736"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Tayeb, Z., Fedjaev, J., Ghaboosi, N., Richter, C., Everding, L., Qu, X., Wu, Y., Cheng, G., and Conradt, J. (2019). Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals. Sensors, 19.","DOI":"10.3390\/s19010210"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Mao, Z., Yao, W., and Huang, Y. (2017, January 25\u201328). EEG-based biometric identification with deep learning. Proceedings of the Neural Engineering (NER), Shanghai, China.","DOI":"10.1109\/NER.2017.8008425"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1109\/TAMD.2015.2431497","article-title":"Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks","volume":"7","author":"Zheng","year":"2015","journal-title":"IEEE Trans. Auton. Ment. Dev."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Zheng, X., Wang, M., and Ordieres-Mere, J. (2018). Comparison of Data Preprocessing Approaches for Applying Deep Learning to Human Activity Recognition in the Context of Industry 4.0. Sensors, 18.","DOI":"10.3390\/s18072146"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1080\/07421222.2000.11045632","article-title":"Measuring the flexibility of information technology infrastructure: Exploratory analysis of a construct","volume":"17","author":"Byrd","year":"2000","journal-title":"J. Manag. Inf. Syst."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"532","DOI":"10.2307\/258557","article-title":"Building theories from case study research","volume":"14","author":"Eisenhardt","year":"1989","journal-title":"Acad. Manag. Rev."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/S0010-9452(72)80014-5","article-title":"The Relationship Between E.E.G. Activity and Handedness","volume":"8","author":"Provins","year":"1972","journal-title":"Cortex"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/0028-3932(71)90067-4","article-title":"The assessment and analysis of handedness: The Edinburgh Inventory","volume":"9","author":"Oldfield","year":"1971","journal-title":"Neuropsychologia"},{"key":"ref_105","unstructured":"Lyons, R. (2004). Understanding Digital Signal Processing, Prentice Hall PTR. [2nd ed.]."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1155\/2011\/156869","article-title":"FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data","volume":"2011","author":"Oostenveld","year":"2011","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"233","DOI":"10.3389\/fpsyg.2012.00233","article-title":"Filter effects and filter artifacts in the analysis of electrophysiological data","volume":"3","author":"Widmann","year":"2012","journal-title":"Front. Psychol."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Winkler, I., Debener, S., Mueller, K., and Tangermann, M. (2015, January 25\u201329). On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milano, Italy.","DOI":"10.1109\/EMBC.2015.7319296"},{"key":"ref_109","unstructured":"Van Rossum, G. (1995). Python Tutorial, Centrum voor Wikunde en Informatica (CWI). Technical Report CS-R9526."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/JBHI.2016.2633287","article-title":"A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices","volume":"21","author":"Ravi","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/13\/2841\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:01:23Z","timestamp":1760187683000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/13\/2841"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,26]]},"references-count":111,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["s19132841"],"URL":"https:\/\/doi.org\/10.3390\/s19132841","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,26]]}}}