{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T21:09:30Z","timestamp":1778274570605,"version":"3.51.4"},"reference-count":112,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:00:00Z","timestamp":1778198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>This paper proposes a high-precision theoretical and computational neurorehabilitation framework for Mild Cognitive Impairment (MCI), connecting computational neuroscience and clinical practice through qEEG-guided neurofeedback training (NFT). By employing sLORETA to identify putative pathological nodes within the Default Mode Network (DMN)\u2014specifically the Precuneus and the Posterior Cingulate\u2014the model utilizes spectral decomposition to isolate the aperiodic 1\/f component, reducing background noise bias and allowing the calculation of a pure individual alpha frequency (IAF) to inform recalibration of Weber\u2019s Cognitive Threshold. The core architecture uses Bayesian algorithms and stochastic modeling to drive a Dynamic Weight Change mechanism. To support Long-Term Potentiation (LTP) and Hebbian learning, reward thresholds are modulated in real time to target a 70% success rate, as a strategic rationale to anticipate neural fatigue while maintaining the Reward Prediction Error required for synaptic strengthening. As a prospective validation pathway, future studies may assess clinical value through changes in MoCA and RAVLT scores, as well as by examining normalization of cortical coherence in the Default Mode Network (DMN). By merging computational neuroscience with biological models of synaptic plasticity, this work outlines how individual biology can be mapped into an explicit mathematical model. The proposed framework may inform an individualized protocol that provides an objective model-based measure of cognitive recovery, suggesting a replicable and robust strategy for neurorehabilitation during the prodromal phase of dementia, and providing a new approach to neuroscience-based cognitive rehabilitation. This work is intended as a theoretical and computational framework; no complete empirical dataset is reported in the present manuscript.<\/jats:p>","DOI":"10.3390\/ijerph23050624","type":"journal-article","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T20:11:13Z","timestamp":1778271073000},"page":"624","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Precision Computational Framework for sLORETA Neurofeedback in Mild Cognitive Impairment: Integration of qEEG Biomarkers and Neuropsychological Metrics"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4611-0101","authenticated-orcid":false,"given":"Viviane","family":"Dasilva","sequence":"first","affiliation":[{"name":"Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6885-4473","authenticated-orcid":false,"given":"Diana","family":"Poli","sequence":"additional","affiliation":[{"name":"Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL (The National Institute for Insurance Against Accidents at Work), 00040 Monte Porzio Catone, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3035-8401","authenticated-orcid":false,"given":"Olimpia","family":"Pino","sequence":"additional","affiliation":[{"name":"Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,8]]},"reference":[{"key":"ref_1","unstructured":"Chartier, D.R., Dellinger, M.B., Evans, J.R., and Budzynski, H.K. (2023). Origins of Electroencephalogram Rhythms and Implications of Neurofeedback. Introduction to Quantitative EEG and Neurofeedback, Academic Press. [3rd ed.]."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Mehmood, F., Rehman, S.U., Mehmood, A., and Kim, Y.J. (2025). Advances in AI-Driven EEG Analysis for Neurological and Oculomotor Disorders: A Systematic Review. Biosensors, 16.","DOI":"10.3390\/bios16010015"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Sajno, E., Bartolotta, S., Tuena, C., Cipresso, P., Pedroli, E., and Riva, G. (2023). Machine Learning in Biosignals Processing for Mental Health: A Narrative Review. Front. Psychol., 13.","DOI":"10.3389\/fpsyg.2022.1066317"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1007\/s10548-021-00828-2","article-title":"Computational Models in Electroencephalography","volume":"35","author":"Glomb","year":"2022","journal-title":"Brain Topogr."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wolpaw, J.R., and Wolpaw, E.W. (2012). Brain\u2013Computer Interfaces: Principles and Practice, Oxford University Press.","DOI":"10.1093\/acprof:oso\/9780195388855.001.0001"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"8308","DOI":"10.55248\/gengpi.5.0524.1338","article-title":"The Significance of EEG Neurofeedback Method in the Treatment of Specific Learning Disorders","volume":"5","author":"Filipova","year":"2024","journal-title":"Int. J. Res. Publ. Rev."},{"key":"ref_7","first-page":"e64918","article-title":"Longitudinal Outcomes of Neurofeedback and Hyperbaric Oxygen Therapy in Treating a Traumatic Brain Injury Patient: A Case Report","volume":"16","author":"Peterson","year":"2024","journal-title":"Cureus"},{"key":"ref_8","first-page":"34","article-title":"A Brain\u2013Computer Interface for Audio-Visual Entrainment in Emotional Regulation: Preliminary Evidence of Its Effects","volume":"6","author":"Pino","year":"2016","journal-title":"Online Int. Interdiscip. Res. J."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zeltser, A., Ochneva, A., Riabinina, D., Zakurazhnaya, V., Tsurina, A., Golubeva, E., Berdalin, A., Andreyuk, D., Leonteva, E., and Kostyuk, G. (2024). EEG Techniques with Brain Activity Localization, Specifically LORETA, and Its Applicability in Monitoring Schizophrenia. J. Clin. Med., 13.","DOI":"10.3390\/jcm13175108"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1787","DOI":"10.1016\/j.neurobiolaging.2008.09.020","article-title":"Cortical Sources of Resting EEG Rhythms in Mild Cognitive Impairment and Subjective Memory Complaint","volume":"31","author":"Babiloni","year":"2010","journal-title":"Neurobiol. Aging"},{"key":"ref_11","first-page":"5","article-title":"Standardized Low-Resolution Brain Electromagnetic Tomography (sLORETA): Technical Details","volume":"24","year":"2002","journal-title":"Methods Find. Exp. Clin. Pharmacol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s10484-012-9191-4","article-title":"The Effects of QEEG-Informed Neurofeedback in ADHD: An Open-Label Pilot Study","volume":"37","author":"Arns","year":"2012","journal-title":"Appl. Psychophysiol. Biofeedback"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tibermacine, I.E., Russo, S., Scarano, G., Tedesco, G., Rabehi, A., and Alhussan, A.A. (2025). Conditional VAE for Personalized Neurofeedback in Cognitive Training. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0335364"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1080\/17470919.2018.1518834","article-title":"The Application of Computational Models to Social Neuroscience: Promises and Pitfalls","volume":"13","author":"Charpentier","year":"2018","journal-title":"Soc. Neurosci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1080\/10874208.2010.545760","article-title":"Standards of Practice for Neurofeedback and Neurotherapy: A Position Paper of the International Society for Neurofeedback & Research","volume":"15","author":"Hammond","year":"2011","journal-title":"J. Neurother."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"119190","DOI":"10.1016\/j.neuroimage.2022.119190","article-title":"Harmonized-Multinational qEEG Norms (HarMNqEEG)","volume":"256","author":"Li","year":"2022","journal-title":"NeuroImage"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"135","DOI":"10.15540\/nr.9.3.135","article-title":"A Feasibility Study of LORETA Z-Score Neurofeedback Training in Adults with Schizophrenia-Spectrum Disorder Experiencing Treatment-Resistant Auditory Verbal Hallucinations","volume":"9","author":"Amico","year":"2022","journal-title":"NeuroRegulation"},{"key":"ref_18","first-page":"e2022248","article-title":"Engagement and Arousal Effects in Predicting the Increase of Cognitive Functioning Following a Neuromodulation Program","volume":"93","author":"Pino","year":"2022","journal-title":"Acta Biomed."},{"key":"ref_19","first-page":"e2021408","article-title":"A Randomized Controlled Trial to Explore the Effect of Audio-Visual Entrainment among Psychological Disorders","volume":"92","author":"Pino","year":"2021","journal-title":"Acta Biomed."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1080\/10874200903543963","article-title":"EEG Biofeedback Case Studies Using Live Z-Score Training and a Normative Database","volume":"14","author":"Collura","year":"2010","journal-title":"J. Neurother."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"149","DOI":"10.15540\/nr.2.3.149","article-title":"A Review of qEEG-Guided Neurofeedback","volume":"2","author":"Wigton","year":"2015","journal-title":"NeuroRegulation"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"18","DOI":"10.15540\/nr.7.1.18","article-title":"Predictors of Neurofeedback Outcomes Following qEEG Individualized Protocols for Anxiety","volume":"7","author":"Gregory","year":"2020","journal-title":"NeuroRegulation"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s10339-024-01181-5","article-title":"Hierarchies of Description Enable Understanding of Cognitive Phenomena in Terms of Neuron Activity","volume":"25","author":"Coward","year":"2024","journal-title":"Cogn. Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1109\/TBME.2019.2913928","article-title":"Electrophysiological Brain Connectivity: Theory and Implementation","volume":"66","author":"He","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Marzola, P., Melzer, T., Pavesi, E., Gil-Mohapel, J., and Brocardo, P.S. (2023). Exploring the Role of Neuroplasticity in Development, Aging, and Neurodegeneration. Brain Sci., 13.","DOI":"10.3390\/brainsci13121610"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"93","DOI":"10.15540\/nr.6.2.93","article-title":"Efficacy of Live Z-Score Neurofeedback Training for Chronic Insomnia: A Single-Case Study","volume":"6","author":"Carrobles","year":"2019","journal-title":"NeuroRegulation"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1080\/02643294.2017.1314264","article-title":"The Cart before the Horse: When Cognitive Neuroscience Precedes Cognitive Neuropsychology","volume":"34","author":"Agis","year":"2017","journal-title":"Cogn. Neuropsychol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/s12984-024-01308-x","article-title":"NSF DARE, Transforming Modeling in Neurorehabilitation: Perspectives and Opportunities from US Funding Agencies","volume":"21","author":"Hwang","year":"2024","journal-title":"J. NeuroEng. Rehabil."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Marlats, F., Bao, G., Chevallier, S., Boubaya, M., Djabelkhir-Jemmi, L., Wu, Y.-H., Lenoir, H., Rigaud, A.-S., and Azabou, E. (2020). SMR\/Theta Neurofeedback Training Improves Cognitive Performance and EEG Activity in Elderly with Mild Cognitive Impairment: A Pilot Study. Front. Aging Neurosci., 12.","DOI":"10.3389\/fnagi.2020.00147"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1007\/s11604-023-01526-8","article-title":"Diagnostic Approach with Z-Score Mapping to Reduce Artifacts Caused by Cerebral Atrophy in Regional CBF Assessment of MCI and Alzheimer\u2019s Disease by [99mTc]-ECD and SPECT","volume":"42","author":"Odano","year":"2024","journal-title":"Jpn. J. Radiol."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Thatcher, R., and Lubar, J.F. (2009). History of the Scientific Standards of QEEG Normative Databases. Introduction to Quantitative EEG and Neurofeedback, Elsevier.","DOI":"10.1016\/B978-0-12-374534-7.00002-2"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.clinph.2023.10.011","article-title":"Functional Connectivity Changes in Mild Cognitive Impairment: A Meta-Analysis of M\/EEG Studies","volume":"156","author":"Buzi","year":"2023","journal-title":"Clin. Neurophysiol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1038\/30918","article-title":"Collective Dynamics of \u2018Small-World\u2019 Networks","volume":"393","author":"Watts","year":"1998","journal-title":"Nature"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e16424","DOI":"10.1111\/ene.16424","article-title":"Analysis of Individual Alpha Frequency in a Large Cohort from a Tertiary Memory Center","volume":"31","author":"Cecchetti","year":"2024","journal-title":"Eur. J. Neurol."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"2469","DOI":"10.1016\/j.neuron.2023.04.023","article-title":"20 years of the default mode network: A review and synthesis","volume":"111","author":"Menon","year":"2023","journal-title":"Neuron"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Azarias, F.R., Almeida, G.H.D.R., de Melo, L.F., Rici, R.E.G., and Maria, D.A. (2025). The journey of the Default Mode Network: Development, function, and impact on mental health. Biology, 14.","DOI":"10.3390\/biology14040395"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.neuroimage.2019.03.073","article-title":"Distinct patterns of default mode and executive control network circuitry contribute to present and future executive function in older adults","volume":"195","author":"Brown","year":"2019","journal-title":"NeuroImage"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1523\/JNEUROSCI.4227-13.2014","article-title":"Precuneus Is a Functional Core of the Default-Mode Network","volume":"34","author":"Utevsky","year":"2014","journal-title":"J. Neurosci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1093\/brain\/awl004","article-title":"The Precuneus: A Review of Its Functional Anatomy and Behavioural Correlates","volume":"129","author":"Cavanna","year":"2006","journal-title":"Brain"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1097\/WNP.0000000000000411","article-title":"Relationship Between Alpha Rhythm and the Default Mode Network: An EEG-fMRI Study","volume":"34","author":"Bowman","year":"2017","journal-title":"J. Clin. Neurophysiol."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Moretti, D.V. (2015). Theta and Alpha EEG Frequency Interplay in Subjects with Mild Cognitive Impairment: Evidence from EEG, MRI, and SPECT Brain Modifications. Front. Aging Neurosci., 7.","DOI":"10.3389\/fnagi.2015.00031"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Tazaki, M. (2024). A review: Effects of neurofeedback on patients with mild cognitive impairment (MCI), and Alzheimer\u2019s disease (AD). Front. Hum. Neurosci., 17.","DOI":"10.3389\/fnhum.2023.1331436"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Su, R., Li, X., Xie, P., and Yuan, Y. (2025). Neurofeedback training modulates brain functional networks and improves cognition in Amnestic Mild Cognitive Impairment patients aged 60\u201370 years. Brain Sci., 15.","DOI":"10.3390\/brainsci15111243"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Echeverri-Ocampo, I., Ardila, K., Molina-Mateo, J., Padilla-Buritica, J.I., Carceller, H., Barcel\u00f3-Martinez, E.A., Llamur, S.I., and Iglesia-Vaya, M.D.L. (2023). EEG-Based Functional Connectivity Analysis for Cognitive Impairment Classification. Electronics, 12.","DOI":"10.3390\/electronics12214432"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Jiang, J., Zhang, J., Li, C., Yu, Z., Yan, Z., and Jiang, J. (2022). Development of a Machine Learning Model to Discriminate Mild Cognitive Impairment Subjects from Normal Controls in Community Screening. Brain Sci., 12.","DOI":"10.3390\/brainsci12091149"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"23","DOI":"10.31887\/DCNS.2016.18.1\/wschultz","article-title":"Dopamine Reward Prediction Error Coding","volume":"18","author":"Schultz","year":"2016","journal-title":"Dialogues Clin. Neurosci."},{"key":"ref_48","unstructured":"Csikszentmihalyi, M. (1990). Flow: The Psychology of Optimal Experience, Harper & Row."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4646","DOI":"10.1038\/s41467-019-12552-4","article-title":"The Eighty Five Percent Rule for Optimal Learning","volume":"10","author":"Wilson","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1158","DOI":"10.1038\/srep01158","article-title":"A Weber-Like Law for Perceptual Learning","volume":"3","author":"Astle","year":"2013","journal-title":"Sci. Rep."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Egner, T. (2017). Cognitive Control and the Ageing Brain. The Wiley Handbook of Cognitive Control, Wiley Online Library.","DOI":"10.1002\/9781118920497"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Thatcher, R.W., North, D.M., and Biver, C.J. (2014). LORETA EEG Phase Reset of the Default Mode Network. Front. Hum. Neurosci., 8.","DOI":"10.3389\/fnhum.2014.00529"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"148989","DOI":"10.1016\/j.brainres.2024.148989","article-title":"A Comparative Study of the Dorsolateral Prefrontal Cortex Targeting Approaches for Transcranial Magnetic Stimulation Treatment: Insights from the Healthy Control Data","volume":"1838","author":"Kinjo","year":"2024","journal-title":"Brain Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1016\/j.tics.2010.09.001","article-title":"The Functional Role of Cross-Frequency Coupling","volume":"14","author":"Canolty","year":"2010","journal-title":"Trends Cogn. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3962","DOI":"10.1523\/JNEUROSCI.4250-04.2005","article-title":"Phase Synchrony Among Neuronal Oscillations in the Human Cortex","volume":"25","author":"Palva","year":"2005","journal-title":"J. Neurosci."},{"key":"ref_56","unstructured":"Porges, S.W. (2011). The Polyvagal Theory: Neurophysiological Foundations of Emotions, Attachment, Communication, and Self-Regulation, W.W. Norton & Company."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1037\/0033-2909.116.2.220","article-title":"Dual-Task Interference in Simple Tasks: Data and Theory","volume":"116","author":"Pashler","year":"1994","journal-title":"Psychol. Bull."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1111\/j.1532-5415.2005.53221.x","article-title":"The Montreal Cognitive Assessment (MoCA): A Brief Screening Tool for Mild Cognitive Impairment","volume":"53","author":"Nasreddine","year":"2005","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_59","unstructured":"Rey, A. (1964). L\u2019Examen Clinique en Psychologie, Presses Universitaires de France."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Kreutzer, J.S., DeLuca, J., and Caplan, B. (2011). Rey Auditory Verbal Learning Test (Rey AVLT). Encyclopedia of Clinical Neuropsychology, Springer.","DOI":"10.1007\/978-0-387-79948-3"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"25","DOI":"10.2174\/1874943720150601E001","article-title":"Memory impairments and rehabilitation: Evidence-based effects of approaches and training programs","volume":"8","author":"Pino","year":"2015","journal-title":"Open Rehabil. J."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Bauer, R., and Gharabaghi, A. (2015). Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: A Bayesian simulation. Front. Neurosci., 9.","DOI":"10.3389\/fnins.2015.00036"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1177\/1550059419874945","article-title":"Standardization and Personalized Medicine Using Quantitative EEG in Clinical Settings","volume":"52","author":"Keizer","year":"2021","journal-title":"Clin. EEG Neurosci."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.1126\/science.3576224","article-title":"Human Cerebral Hemispheres Develop at Different Rates and Ages","volume":"236","author":"Thatcher","year":"1987","journal-title":"Science"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2956","DOI":"10.1109\/TNSRE.2025.3592988","article-title":"Adaptive Neurofeedback Training Using a Virtual Reality Game Enhances Motor Imagery Performance in Brain\u2013Computer Interfaces","volume":"33","author":"Wang","year":"2025","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1146\/annurev-neuro-071013-014030","article-title":"The Brain\u2019s Default Mode Network","volume":"38","author":"Raichle","year":"2015","journal-title":"Annu. Rev. Neurosci."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"e3414","DOI":"10.1002\/brb3.3414","article-title":"Alterations in Brain Functional Connectivity in Patients with Mild Cognitive Impairment: A Systematic Review and Meta-Analysis of fNIRS Studies","volume":"14","author":"Wang","year":"2024","journal-title":"Brain Behav."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1016\/j.neubiorev.2021.10.013","article-title":"Evidence for Exercise-Related Plasticity in Functional and Structural Neural Network Connectivity","volume":"131","author":"Won","year":"2021","journal-title":"Neurosci. Biobehav. Rev."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.neubiorev.2015.07.014","article-title":"Resting-State Functional Connectivity in Major Depressive Disorder: A Review","volume":"56","author":"Mulders","year":"2015","journal-title":"Neurosci. Biobehav. Rev."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/S0167-8760(97)00773-3","article-title":"EEG-Alpha Rhythms and Memory Processes","volume":"26","author":"Klimesch","year":"1997","journal-title":"Int. J. Psychophysiol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"7326","DOI":"10.1523\/JNEUROSCI.4816-14.2015","article-title":"Consolidation of Associative and Item Memory Is Related to Post-Encoding Functional Connectivity between the Ventral Tegmental Area and Different Medial Temporal Lobe Subregions during an Unrelated Task","volume":"35","author":"Tompary","year":"2015","journal-title":"J. Neurosci."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1016\/j.neuroimage.2006.11.004","article-title":"Enhanced Detection of Artifacts in EEG Data Using Higher-order Statistics and Independent Component Analysis","volume":"34","author":"Delorme","year":"2007","journal-title":"NeuroImage"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1113\/jphysiol.1952.sp004764","article-title":"A quantitative description of membrane current and its application to conduction and excitation in nerve","volume":"117","author":"Hodgkin","year":"1952","journal-title":"J. Physiol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/0065-2571(65)90067-1","article-title":"Oscillatory Behavior in Enzymatic Control Processes","volume":"3","author":"Goodwin","year":"1965","journal-title":"Adv. Enzym. Regul."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Paszkiel, S. (2019). Using the LORETA Method for Localization of the EEG Signal Sources in BCI Technology. Analysis and Classification of EEG Signals for Brain\u2013Computer Interfaces, Springer.","DOI":"10.1007\/978-3-030-30581-9_5"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"046004","DOI":"10.1088\/1741-2552\/aa69d1","article-title":"Automated EEG Artifact Elimination by Applying Machine Learning Algorithms to ICA-based Features","volume":"14","author":"Scouten","year":"2017","journal-title":"J. Neural Eng."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Sch\u00f6lzel, C., Blesius, V., Ernst, G., and Dominik, A. (2020). An Understandable, Extensible, and Reusable Implementation of the Hodgkin-Huxley Equations Using Modelica. Front. Physiol., 11.","DOI":"10.3389\/fphys.2020.583203"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Izhikevich, E.M. (2007). Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting, MIT Press.","DOI":"10.7551\/mitpress\/2526.001.0001"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.bspc.2014.01.009","article-title":"A Survey of Methods Used for Source Localization of EEG Signals","volume":"11","author":"Jatoi","year":"2014","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1016\/j.neuroimage.2012.03.048","article-title":"Measuring functional connectivity in MEG: A multivariate approach insensitive to linear source leakage","volume":"63","author":"Brookes","year":"2012","journal-title":"NeuroImage"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.neuroimage.2015.03.071","article-title":"A symmetric multivariate leakage correction for MEG connectomes","volume":"117","author":"Colclough","year":"2015","journal-title":"NeuroImage"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"154","DOI":"10.54254\/2753-8818\/38\/20240589","article-title":"Utilizing Fast Fourier Transform in the Processing of Biomedical Signals: An Analytical Approach","volume":"38","author":"Liu","year":"2024","journal-title":"Theor. Nat. Sci."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"154","DOI":"10.3934\/Neuroscience.2023012","article-title":"Electroencephalography and Mild Cognitive Impairment Research: A Scoping Review and Bibliometric Analysis (ScoRBA)","volume":"10","author":"Wijaya","year":"2023","journal-title":"AIMS Neurosci."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"2525389","DOI":"10.1080\/07853890.2025.2525389","article-title":"Early detection of cognitive impairment in the general population through a digital tool targeting the prospective memory. A pilot study","volume":"57","author":"Pino","year":"2025","journal-title":"Ann. Med."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.brainresrev.2005.04.005","article-title":"The Functional Significance of Mu Rhythms: Translating \u201cSeeing\u201d and \u201cHearing\u201d into \u201cDoing\u201d","volume":"50","author":"Pineda","year":"2005","journal-title":"Brain Res. Rev."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/0167-8760(84)90014-X","article-title":"Low Resolution Electromagnetic Tomography: A New Method for Localizing Electrical Activity in the Brain","volume":"18","author":"Michel","year":"1994","journal-title":"Int. J. Psychophysiol."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1038\/nrn756","article-title":"The Problem of Functional Localization in the Human Brain","volume":"3","author":"Brett","year":"2002","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1098\/rstb.2001.0915","article-title":"A Probabilistic Atlas and Reference System for the Human Brain: International Consortium for Brain Mapping (ICBM)","volume":"356","author":"Mazziotta","year":"2001","journal-title":"Philos. Trans. R. Soc. B Biol. Sci."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Ko, J., Park, U., Kim, D., and Kang, S.W. (2021). Quantitative Electroencephalogram Standardization: A Sex- and Age-Differentiated Normative Database. Front. Neurosci., 15.","DOI":"10.3389\/fnins.2021.766781"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Ben-Gal, I., and Kagan, E. (2021). Information Theory: Deep Ideas, Wide Perspectives, and Various Applications. Entropy, 23.","DOI":"10.3390\/e23020232"},{"key":"ref_91","unstructured":"Horenko, I., Bassetti, D., and Posp\u00ed\u0161il, L. (2025). Fast and Close Shannon Entropy Approximations of entropic measures. arxiv."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.clinph.2022.08.010","article-title":"Clinical Applications of EEG Power Spectra Aperiodic Component Analysis: A Mini-Review","volume":"143","author":"Pani","year":"2022","journal-title":"Clin. Neurophysiol."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Donoghue, T., Hammonds, R., Lybrand, E., Waschke, L., Gao, R., and Voytek, B. (2024). Evaluating and Comparing Measures of Aperiodic Neural Activity. bioRxiv, 2024.09.15.613114.","DOI":"10.1101\/2024.09.15.613114"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1523\/JNEUROSCI.02-01-00032.1982","article-title":"Theory for the Development of Neuron Selectivity: Orientation Specificity and Binocular Interaction in Visual Cortex","volume":"2","author":"Bienenstock","year":"1982","journal-title":"J. Neurosci."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"a005736","DOI":"10.1101\/cshperspect.a005736","article-title":"Homeostatic Synaptic Plasticity: Local and Global Mechanisms for Stabilizing Neuronal Function","volume":"4","author":"Turrigiano","year":"2012","journal-title":"Cold Spring Harb. Perspect. Biol."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1152\/physrev.00030.2005","article-title":"Spike-Timing-Dependent Plasticity: From Synapse to Perception","volume":"86","author":"Dan","year":"2006","journal-title":"Physiol. Rev."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1111\/psyp.12586","article-title":"Resting State Connectivity of the Medial Prefrontal Cortex Covaries with Individual Differences in High-Frequency Heart Rate Variability","volume":"53","author":"Jennings","year":"2016","journal-title":"Psychophysiology"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Hsiao, F.-J., Wang, Y.-J., Yan, S.-H., Chen, W.-T., and Lin, Y.-Y. (2013). Altered Oscillation and Synchronization of Default-Mode Network Activity in Mild Alzheimer\u2019s Disease Compared to Mild Cognitive Impairment: An Electrophysiological Study. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0068792"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"110133","DOI":"10.1016\/j.neuropharm.2024.110133","article-title":"Dopaminergic System and Neurons: Role in Multiple Neurological Diseases","volume":"260","author":"Chen","year":"2024","journal-title":"Neuropharmacology"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1016\/j.apmr.2020.04.010","article-title":"Interaction Between Recovery of Motor and Language Abilities after Stroke","volume":"101","author":"Ginex","year":"2020","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Benso, F., Moretti, S., Bellazzini, V., Benso, E., Ardu, E., and Gazzellini, S. (2021). Principles of Integrated Cognitive Training for Executive Attention: Application to an Instrumental Skill. Front. Psychol., 12.","DOI":"10.3389\/fpsyg.2021.647749"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Maikusa, N., Shigemoto, Y., Chiba, E., Kimura, Y., Matsuda, H., and Sato, N. (2022). Harmonized Z-Scores Calculated from a Large-Scale Normal MRI Database to Evaluate Brain Atrophy in Neurodegenerative Disorders. J. Pers. Med., 12.","DOI":"10.3390\/jpm12101555"},{"key":"ref_103","unstructured":"Schmidt, M. (1996). Rey Auditory Verbal Learning Test: A Handbook, Western Psychological Services."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Arakaki, X., Arechavala, R.J., Choy, E.H., Bautista, J., Bliss, B., Molloy, C., Wu, D.A., Shimojo, S., Jiang, Y., and Kleinman, M.T. (2023). The Connection Between Heart Rate Variability (HRV), Neurological Health, and Cognition: A Literature Review. Front. Neurosci., 17.","DOI":"10.3389\/fnins.2023.1055445"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1007\/s10484-024-09624-7","article-title":"Short-Term Effects of Heart Rate Variability Biofeedback on Working Memory","volume":"49","author":"Bahameish","year":"2024","journal-title":"Appl. Psychophysiol. Biofeedback"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Yao, S., Zhu, J., Li, S., Zhang, R., Zhao, J., Yang, X., and Wang, Y. (2022). Bibliometric Analysis of Quantitative Electroencephalogram Research in Neuropsychiatric Disorders from 2000 to 2021. Front. Psychiatry, 13.","DOI":"10.3389\/fpsyt.2022.830819"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"47001","DOI":"10.1209\/0295-5075\/adb3c9","article-title":"Ten Years of the Digital Twin Brain: Perspectives and Challenges","volume":"149","author":"Guo","year":"2025","journal-title":"Europhys. Lett."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1038\/s42254-023-00595-y","article-title":"A New Frontier for Hopfield Networks","volume":"5","author":"Krotov","year":"2023","journal-title":"Nat. Rev. Phys."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Warren, T.S., Shende, S.A., Ashrafi, J., Clements, G.M., and Mudar, R.A. (2025). Resting-State EEG Power and Aperiodic Activity in Individuals with Mild Cognitive Impairment and Cognitively Healthy Controls. Brain Sci., 15.","DOI":"10.3390\/brainsci15121305"},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Delorme, A., and Makeig, S. (2023). This is no \u201cICA bug\u201d: Response to the article, \u201cICA\u2019s bug: How ghost ICs emerge from effective rank deficiency caused by EEG electrode interpolation and incorrect re-referencing\u201d. Front. Neuroimaging, 2.","DOI":"10.3389\/fnimg.2023.1331404"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"2214","DOI":"10.1093\/brain\/awac435","article-title":"Direct electrical brain stimulation of human memory: Lessons learnt and future perspectives","volume":"146","author":"Kucewicz","year":"2023","journal-title":"Brain"},{"key":"ref_112","first-page":"adb8ef","article-title":"On the feasibility of an online brain-computer interface-based neurofeedback game for enhancing attention and working memory in stroke and Mild Cognitive Impairment patients","volume":"11","author":"Suhail","year":"2025","journal-title":"Biomed. Phys. Eng. Express"}],"container-title":["International Journal of Environmental Research and Public Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1660-4601\/23\/5\/624\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T20:12:39Z","timestamp":1778271159000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1660-4601\/23\/5\/624"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,8]]},"references-count":112,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["ijerph23050624"],"URL":"https:\/\/doi.org\/10.3390\/ijerph23050624","relation":{},"ISSN":["1660-4601"],"issn-type":[{"value":"1660-4601","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,8]]}}}