{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T15:20:01Z","timestamp":1781277601050,"version":"3.54.1"},"reference-count":71,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T00:00:00Z","timestamp":1763683200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomimetics"],"abstract":"<jats:p>Alzheimer\u2019s disease (AD) is the most prevalent form of dementia, and accessible biomarkers for early detection remain limited. This study introduces a biomimetic approach in which brain electrical activity is transformed into sound and vibration, emulating natural sensory encoding mechanisms. Resting-state EEG recordings from 36 AD patients and 29 healthy controls were averaged by group, directly sonified, and used to drive a membrane\u2013laser system that projected dynamic vibrational patterns. This transformation mirrors how biological systems convert electrical signals into sensory representations, offering a novel bridge between neural dynamics and physical patterns. The resulting videos were processed through adaptive binarization, morphological filtering, and contour-based masking. Quantitative descriptors such as active area, spatial entropy, fractal dimension, and centroid dynamics were extracted, capturing group-specific differences. A Random Forest classifier trained on these features achieved an accuracy of 0.85 and an AUC of 0.93 in distinguishing AD from controls. These findings suggest that EEG sonification combined with vibrational projection provides not only a novel non-invasive biomarker candidate but also a biomimetic framework inspired by the brain\u2019s own capacity to encode and represent complex signals.<\/jats:p>","DOI":"10.3390\/biomimetics10120792","type":"journal-article","created":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T09:50:26Z","timestamp":1763718626000},"page":"792","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["The Vibrational Signature of Alzheimer\u2019s Disease: A Computational Approach Based on Sonification, Laser Projection, and Computer Vision Analysis"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9606-3791","authenticated-orcid":false,"given":"Rub\u00e9n","family":"P\u00e9rez-Elvira","sequence":"first","affiliation":[{"name":"Department of Psychobiology, Faculty of Psychology, Pontifical University of Salamanca, 37002 Salamanca, Spain"},{"name":"Neuropsychophysiology Laboratory, NEPSA Rehabilitaci\u00f3n Neurol\u00f3gica, 37003 Salamanca, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5966-8556","authenticated-orcid":false,"given":"Javier","family":"Oltra-Cucarella","sequence":"additional","affiliation":[{"name":"Department of Health Psychology, Miguel Hern\u00e1ndez University, 03202 Elche, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mar\u00eda","family":"Agudo Juan","sequence":"additional","affiliation":[{"name":"Neuropsychophysiology Laboratory, NEPSA Rehabilitaci\u00f3n Neurol\u00f3gica, 37003 Salamanca, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8349-0441","authenticated-orcid":false,"given":"Luis","family":"Polo-Ferrero","sequence":"additional","affiliation":[{"name":"Department of Nursing and Physiotherapy, University of Salamanca, 37007 Salamanca, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3597-2048","authenticated-orcid":false,"given":"Ra\u00fal","family":"Ju\u00e1rez-Vela","sequence":"additional","affiliation":[{"name":"Department of Nursing, Faculty of Health Sciences, University of La Rioja, 26004 Logro\u00f1o, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7168-5551","authenticated-orcid":false,"given":"Jorge","family":"Bosch-Bayard","sequence":"additional","affiliation":[{"name":"Faculty of Psychology, University of Oldenburg, 26111 Oldenburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4852-4148","authenticated-orcid":false,"given":"Manuel","family":"Quintana D\u00edaz","sequence":"additional","affiliation":[{"name":"Intensive Care Unit, La Paz University Hospital, 28046 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3423-6367","authenticated-orcid":false,"given":"Jorge","family":"de la Cruz","sequence":"additional","affiliation":[{"name":"Huanjiang Laboratory, Center of Research on Microgrids (CROM), Zhuji 311800, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3534-2276","authenticated-orcid":false,"given":"Alfonso","family":"Salgado Ru\u00edz","sequence":"additional","affiliation":[{"name":"Department of Psychobiology, Faculty of Psychology, Pontifical University of Salamanca, 37002 Salamanca, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"97","DOI":"10.14283\/jpad.2023.61","article-title":"Burden of Illness in People with Alzheimer\u2019s Disease: A Systematic Review of Epidemiology, Comorbidities and Mortality","volume":"11","author":"Eichinger","year":"2024","journal-title":"J. Prev. Alzheimers Dis."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1007\/s12035-023-03578-3","article-title":"Non-Invasive Biomarkers for Early Detection of Alzheimer\u2019s Disease: A New-Age Perspective","volume":"61","author":"Chimthanawala","year":"2024","journal-title":"Mol. Neurobiol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e14335","DOI":"10.1002\/alz.14335","article-title":"The Alzheimer\u2019s Association Clinical Practice Guideline for the Diagnostic Evaluation, Testing, Counseling, and Disclosure of Suspected Alzheimer\u2019s Disease and Related Disorders (DETeCD-ADRD): Validated Clinical Assessment Instruments","volume":"21","author":"Atri","year":"2025","journal-title":"Alzheimers Dement."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Alzola, P., Carnero, C., Bermejo-Pareja, F., S\u00e1nchez-Benavides, G., Pe\u00f1a-Casanova, J., Puertas-Mart\u00edn, V., Fern\u00e1ndez-Calvo, B., and Contador, I. (2024). Neuropsychological Assessment for Early Detection and Diagnosis of Dementia: Current Knowledge and New Insights. J. Clin. Med., 13.","DOI":"10.3390\/jcm13123442"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1111\/jgs.15412","article-title":"For the Alzheimer\u2019s Disease Neuroimaging Initiative Using Base Rate of Low Scores to Identify Progression from Amnestic Mild Cognitive Impairment to Alzheimer\u2019s Disease","volume":"66","author":"Lipnicki","year":"2018","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e14363","DOI":"10.1002\/alz.14363","article-title":"The Role of Neuropsychological Assessment in the Evaluation of Patients with Cognitive-behavioral Change Due to Suspected Alzheimer\u2019s Disease and Other Causes of Cognitive Impairment and Dementia","volume":"21","author":"Shaughnessy","year":"2025","journal-title":"Alzheimers Dement."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"a006213","DOI":"10.1101\/cshperspect.a006213","article-title":"Brain Imaging in Alzheimer Disease","volume":"2","author":"Johnson","year":"2012","journal-title":"Cold Spring Harb. Perspect. Med."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Du, X., Chen, W., Zhang, T., and Xu, Z. (2023). Advances in Diagnosing Mild Cognitive Impairment and Alzheimer\u2019s Disease Using 11C-PIB- PET\/CT and Common Neuropsychological Tests. Front. Neurosci., 17.","DOI":"10.3389\/fnins.2023.1216215"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"14","DOI":"10.25259\/JNRP_431_2024","article-title":"From Ambiguity to Accuracy: A Review of Alzheimer\u2019s Disease Diagnostic Errors and the Need for Non-Invasive Biomarkers","volume":"16","author":"Rapaka","year":"2025","journal-title":"J. Neurosci. Rural Pract."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1186\/s13195-024-01582-w","article-title":"EEG Biomarkers in Alzheimer\u2019s and Prodromal Alzheimer\u2019s: A Comprehensive Analysis of Spectral and Connectivity Features","volume":"16","author":"Chetty","year":"2024","journal-title":"Alzheimers Res. Ther."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4485","DOI":"10.1212\/WNL.0000000000211707","article-title":"Utility of the P300 EEG Biomarker in the Early Identification of Alzheimer\u2019s Disease: A Systematic Review (P1-3.005)","volume":"104","author":"Yamauchi","year":"2025","journal-title":"Neurology"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yuan, Y., and Zhao, Y. (2025). The Role of Quantitative EEG Biomarkers in Alzheimer\u2019s Disease and Mild Cognitive Impairment: Applications and Insights. Front. Aging Neurosci., 17.","DOI":"10.3389\/fnagi.2025.1522552"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1528","DOI":"10.1002\/alz.12311","article-title":"Measures of Resting State EEG Rhythms for Clinical Trials in Alzheimer\u2019s Disease: Recommendations of an Expert Panel","volume":"17","author":"Babiloni","year":"2021","journal-title":"Alzheimers Dement."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/S1388-2457(03)00345-6","article-title":"Individual Analysis of EEG Frequency and Band Power in Mild Alzheimer\u2019s Disease","volume":"115","author":"Moretti","year":"2004","journal-title":"Clin. Neurophysiol."},{"key":"ref_15","first-page":"8915079","article-title":"Complexity Measures for Quantifying Changes in Electroencephalogram in Alzheimers Disease","volume":"2018","author":"Jammeh","year":"2022","journal-title":"Complexity"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"\u015eeker, M., \u00d6zbek, Y., Yener, G., and \u00d6zerdem, M.S. (2021). Complexity of EEG Dynamics for Early Diagnosis of Alzheimer\u2019s Disease Using Permutation Entropy Neuromarker. Comput. Methods Programs Biomed., 206.","DOI":"10.1016\/j.cmpb.2021.106116"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Paitel, E.R., Otteman, C.B.D., Polking, M.C., Licht, H.J., and Nielson, K.A. (2025). Functional and Effective EEG Connectivity Patterns in Alzheimer\u2019s Disease and Mild Cognitive Impairment: A Systematic Review. Front. Aging Neurosci., 17.","DOI":"10.3389\/fnagi.2025.1496235"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Aslan, U., and Ak\u015fahin, M.F. (2025). Detection of Alzheimer and Mild Cognitive Impairment Patients by Poincare and Entropy Methods Based on Electroencephalography Signals. Biomed. Eng. OnLine, 24.","DOI":"10.1186\/s12938-025-01369-6"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2096","DOI":"10.1093\/brain\/awz150","article-title":"EEG Evidence of Compensatory Mechanisms in Preclinical Alzheimer\u2019s Disease","volume":"142","author":"Gaubert","year":"2019","journal-title":"Brain"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Choi, J., Ku, B., Doan, D.N.T., Park, J., Cha, W., Kim, J.U., and Lee, K.H. (2023). Prefrontal EEG Slowing, Synchronization, and ERP Peak Latency in Association with Predementia Stages of Alzheimer\u2019s Disease. Front. Aging Neurosci., 15.","DOI":"10.3389\/fnagi.2023.1131857"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"013005","DOI":"10.1103\/PRXLife.3.013005","article-title":"Harnessing Long-Range Temporal Correlations for Advanced Epilepsy Classification","volume":"3","author":"Grubov","year":"2025","journal-title":"PRX Life"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chaddad, A., Wu, Y., Kateb, R., and Bouridane, A. (2023). Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques. Sensors, 23.","DOI":"10.3390\/s23146434"},{"key":"ref_23","first-page":"67","article-title":"The Applied Principles of EEG Analysis Methods in Neuroscience and Clinical Neurology","volume":"10","author":"Zhang","year":"2023","journal-title":"Mil. Med. Res."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Finnis, R., Mehmood, A., Holle, H., and Iqbal, J. (2025). Exploring Imagined Movement for Brain\u2013Computer Interface Control: An fNIRS and EEG Review. Brain Sci., 15.","DOI":"10.3390\/brainsci15091013"},{"key":"ref_25","unstructured":"Hermann, T., Meinicke, P., Ritter, H., M\u00fcller, H.M., and Weiss, S. (2002, January 2\u20135). Sonifications for EEG Data Analysis. Proceedings of the 2002 International Conference on Auditory Display, Kyoto, Japan."},{"key":"ref_26","first-page":"292","article-title":"Audio Representations of Multi-Channel EEG: A New Tool for Diagnosis of Brain Disorders","volume":"1","author":"Vialatte","year":"2012","journal-title":"Am. J. Neurodegener. Dis."},{"key":"ref_27","unstructured":"Kramer, G., Walker, B., and Bargar, R. (1999). Sonification Report: Status of the Field and Research Agenda, International Community for Auditory Display."},{"key":"ref_28","unstructured":"Miranda, E., and Brouse, A. (2005, January 26\u201328). Toward Direct Brain-Computer Musical Interfaces. Proceedings of the 2005 Conference on New Interfaces for Musical Expression, Vancouver, BC, Canada."},{"key":"ref_29","first-page":"69","article-title":"Music Generation by Direct Sonification and Musification Using EEG Data","volume":"5","author":"Inoue","year":"2023","journal-title":"Int. J. Music Sci. Technol. Art"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s11571-018-9502-4","article-title":"Music of Brain and Music on Brain: A Novel EEG Sonification Approach","volume":"13","author":"Sanyal","year":"2019","journal-title":"Cogn. Neurodyn."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Miltiadous, A., Tzimourta, K.D., Afrantou, T., Ioannidis, P., Grigoriadis, N., Tsalikakis, D.G., Angelidis, P., Tsipouras, M.G., Glavas, E., and Giannakeas, N. (2023). A Dataset of Scalp EEG Recordings of Alzheimer\u2019s Disease, Frontotemporal Dementia and Healthy Subjects from Routine EEG. Data, 8.","DOI":"10.3390\/data8060095"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Buzs\u00e1ki, G. (2006). Rhythms of the Brain, Oxford University Press.","DOI":"10.1093\/acprof:oso\/9780195301069.001.0001"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1490","DOI":"10.1016\/j.clinph.2004.01.001","article-title":"EEG Dynamics in Patients with Alzheimer\u2019s Disease","volume":"115","author":"Jeong","year":"2004","journal-title":"Clin. Neurophysiol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Nunez, P.L., and Srinivasan, R. (2006). Electric Fields of the Brain: The Neurophysics of EEG, Oxford University Press. [2nd ed.].","DOI":"10.1093\/acprof:oso\/9780195050387.001.0001"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1114","DOI":"10.1109\/TBME.2019.2930186","article-title":"Evaluation of Artifact Subspace Reconstruction for Automatic Artifact Components Removal in Multi-Channel EEG Recordings","volume":"67","author":"Chang","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_36","first-page":"122","article-title":"The OpenCV Library","volume":"120","author":"Bradsky","year":"2000","journal-title":"Dr Dobb\u2019s J. Softw. Tools"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yoon, J., and Choi, M.-K. (2023, January 17\u201324). Exploring Video Frame Redundancies for Efficient Data Sampling and Annotation in Instance Segmentation. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Vancouver, BC, Canada.","DOI":"10.1109\/CVPRW59228.2023.00333"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1080\/2151237X.2007.10129236","article-title":"Adaptive Thresholding Using the Integral Image","volume":"12","author":"Bradley","year":"2007","journal-title":"J. Graph. Tools"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Jamil, N., Sembok, T.M.T., and Bakar, Z.A. (2008, January 26\u201329). Noise Removal and Enhancement of Binary Images Using Morphological Operations. Proceedings of the 2008 International Symposium on Information Technology, Kuala Lumpur, Malaysia.","DOI":"10.1109\/ITSIM.2008.4631954"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"687","DOI":"10.30574\/wjarr.2022.14.3.0576","article-title":"Opening and Closing in Morphological Image Processing","volume":"14","author":"Bhutada","year":"2022","journal-title":"World J. Adv. Res. Rev."},{"key":"ref_41","unstructured":"Rama Bai, M., Venkata Krishna, V., and SreeDevi, J. (2010). International Journal of Computer Science Issues, SoftwareFirst, Ltd."},{"key":"ref_42","unstructured":"Sivasakthi, S. (2025, November 14). A Study on Morphological Image Processing and Its Operations. Available online: https:\/\/www.researchgate.net\/publication\/354598374_A_STUDY_ON_MORPHOLOGICAL_IMAGE_PROCESSING_AND_ITS_OPERATIONS."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chang, J., Gao, X., and Wang, N. (2021). Object-Oriented Building Contour Optimization Methodology for Image Classification Results via Generalized Gradient Vector Flow Snake Model. Remote Sens., 13.","DOI":"10.3390\/rs13122406"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A Mathematical Theory of Communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"116984","DOI":"10.1016\/j.measurement.2025.116984","article-title":"Prediction of Fuel Consumption and Shaft Torque Using Machine Learning and Analysis of Engine Curve Diagrams","volume":"248","author":"Park","year":"2025","journal-title":"Measurement"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Karim, A., Ryu, S., and Jeong, I.C. (2025). Ensemble Learning for Biomedical Signal Classification: A High-Accuracy Framework Using Spectrograms from Percussion and Palpation. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-05027-8"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"122778","DOI":"10.1016\/j.eswa.2023.122778","article-title":"A Review of Ensemble Learning and Data Augmentation Models for Class Imbalanced Problems: Combination, Implementation and Evaluation","volume":"244","author":"Khan","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ramezan, C.A., Warner, T.A., and Maxwell, A.E. (2019). Evaluation of Sampling and Cross-Validation Tuning Strategies for Regional-Scale Machine Learning Classification. Remote Sens., 11.","DOI":"10.3390\/rs11020185"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Szeghalmy, S., and Fazekas, A. (2023). A Comparative Study of the Use of Stratified Cross-Validation and Distribution-Balanced Stratified Cross-Validation in Imbalanced Learning. Sensors, 23.","DOI":"10.3390\/s23042333"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"He, Z., Sirak Rakin, A., and Deliang, F. (2019, January 15\u201320). Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness Against Adversarial Attack. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00068"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4810","DOI":"10.1118\/1.3213517","article-title":"Noise Injection for Training Artificial Neural Networks: A Comparison with Weight Decay and Early Stopping","volume":"36","author":"Zur","year":"2009","journal-title":"Med. Phys."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A Systematic Analysis of Performance Measures for Classification Tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Inf. Process. Manag."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Chicco, D., and Jurman, G. (2020). The Advantages of the Matthews Correlation Coefficient (MCC) over F1 Score and Accuracy in Binary Classification Evaluation. BMC Genom., 21.","DOI":"10.1186\/s12864-019-6413-7"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An Introduction to ROC Analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Fiscon, G., Weitschek, E., Cialini, A., Felici, G., Bertolazzi, P., De Salvo, S., Bramanti, A., Bramanti, P., and De Cola, M.C. (2018). Combining EEG Signal Processing with Supervised Methods for Alzheimer\u2019s Patients Classification. BMC Med. Inform. Decis. Mak., 18.","DOI":"10.1186\/s12911-018-0613-y"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"AlSharabi, K., Salamah, Y.B., Aljalal, M., Abdurraqeeb, A.M., and Alturki, F.A. (2023). EEG-Based Clinical Decision Support System for Alzheimer\u2019s Disorders Diagnosis Using EMD and Deep Learning Techniques. Front. Hum. Neurosci., 17.","DOI":"10.3389\/fnhum.2023.1190203"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Vicchietti, M.L., Ramos, F.M., Betting, L.E., and Campanharo, A.S.L.O. (2023). Computational Methods of EEG Signals Analysis for Alzheimer\u2019s Disease Classification. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-32664-8"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Wu, D., Li, C.-Y., and Yao, D.-Z. (2009). Scale-Free Music of the Brain. PLoS ONE, 4.","DOI":"10.1371\/journal.pone.0005915"},{"key":"ref_60","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_61","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.3233\/JAD-220508","article-title":"Systematic Review of EEG Coherence in Alzheimer\u2019s Disease","volume":"91","author":"Fischer","year":"2023","journal-title":"J. Alzheimers Dis."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"927573","DOI":"10.4061\/2011\/927573","article-title":"Electroencephalographic Rhythms in Alzheimer\u2019s Disease","volume":"2011","author":"Lizio","year":"2011","journal-title":"Int. J. Alzheimers Dis."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Stylianou, O., Racz, F.S., Kim, K., Kaposzta, Z., Czoch, A., Yabluchanskiy, A., Eke, A., and Mukli, P. (2021). Multifractal Functional Connectivity Analysis of Electroencephalogram Reveals Reorganization of Brain Networks in a Visual Pattern Recognition Paradigm. Front. Hum. Neurosci., 15.","DOI":"10.3389\/fnhum.2021.740225"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Di Ieva, A. (2016). Fractal Fluency: An Intimate Relationship Between the Brain and Processing of Fractal Stimuli. The Fractal Geometry of the Brain, Springer. Springer Series in Computational Neuroscience.","DOI":"10.1007\/978-1-4939-3995-4"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2266","DOI":"10.1016\/j.clinph.2005.06.011","article-title":"Nonlinear Dynamical Analysis of EEG and MEG: Review of an Emerging Field","volume":"116","author":"Stam","year":"2005","journal-title":"Clin. Neurophysiol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1088\/0967-3334\/27\/3\/003","article-title":"Entropy Analysis of the EEG Background Activity in Alzheimer\u2019s Disease Patients","volume":"27","author":"Hornero","year":"2006","journal-title":"Physiol. Meas."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.medengphy.2005.07.004","article-title":"Analysis of EEG Background Activity in Alzheimer\u2019s Disease Patients with Lempel\u2013Ziv Complexity and Central Tendency Measure","volume":"28","author":"Hornero","year":"2006","journal-title":"Med. Eng. Phys."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.neurobiolaging.2004.03.008","article-title":"Decreased EEG Synchronization in Alzheimer\u2019s Disease and Mild Cognitive Impairment","volume":"26","author":"Koenig","year":"2005","journal-title":"Neurobiol. Aging"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.neuroimage.2017.06.061","article-title":"Cross-Validation Failure: Small Sample Sizes Lead to Large Error Bars","volume":"180","author":"Varoquaux","year":"2018","journal-title":"NeuroImage"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nrn.2016.167","article-title":"Scanning the Horizon: Towards Transparent and Reproducible Neuroimaging Research","volume":"18","author":"Poldrack","year":"2017","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"e1","DOI":"10.1017\/S0140525X20001685","article-title":"The Generalizability Crisis","volume":"45","author":"Yarkoni","year":"2022","journal-title":"Behav. Brain Sci."}],"container-title":["Biomimetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-7673\/10\/12\/792\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T10:12:11Z","timestamp":1763719931000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-7673\/10\/12\/792"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,21]]},"references-count":71,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["biomimetics10120792"],"URL":"https:\/\/doi.org\/10.3390\/biomimetics10120792","relation":{},"ISSN":["2313-7673"],"issn-type":[{"value":"2313-7673","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,21]]}}}