A Novel Neurofeedback Paradigm: First Implementation of Cordance-Based Training for Anxiety and Mood Recovery
DOI:
https://doi.org/10.15540/nr.13.1.77Keywords:
Neurofeedback, cordance, Anxiety, depressionAbstract
This study presents the first implementation of a neurofeedback (NF) protocol based on cordance targeting mood and anxiety disorders. Cordance, a multivariate measure of brain activity, integrates both power within frequency bands and interfrequency relationships, providing a unique perspective on neural synchronization and connectivity. Using a single-case design, a 44-year-old male patient with anxiety, depression, and insomnia was selected based on left frontal discordance. Seven NF sessions were conducted, reinforcing increases in cordance in the left anterior quadrant. The results showed significant improvements in psychometric measures, including reductions in depression, anxiety, and insomnia scores, alongside a marked shift in cordance values toward normative levels. This study introduces cordance-based NF as a potential tool for mood and anxiety regulation, offering promising preliminary evidence for its efficacy. Future research should explore larger sample sizes and longer follow-ups to confirm these findings and expand the clinical applications of
cordance-based interventions.
References
Adamczyk, M., Gazea, M., Wollweber, B., Holsboer, F., Dresler, M., Steiger, A., & Pawlowski, M. (2015). Cordance derived from REM sleep EEG as a biomarker for treatment response in depression – A naturalistic study after antidepressant medication. Journal of Psychiatric Research, 63, 97–104. https://doi.org/10.1016/j.jpsychires.2015.02.007
Arns, M., de Ridder, S., Strehl, U., Breteler, M., & Coenen, A. (2009). Efficacy of neurofeedback treatment in ADHD: The effects on inattention, impulsivity and hyperactivity: A meta-analysis. Clinical EEG and Neuroscience, 40(3), 180–189. https://doi.org/10.1177/155005940904000311
Babiloni, C., Blinowska, K., Bonanni, L., Cichocki, A., De Haan, W., Del Percio, C., Dubois, B., Escudero, J., Fernández, A., Frisoni, G., Guntekin, B., Hajos, M., Hampel, H., Ifeachor, E., Kilborn, K., Kumar, S., Johnsen, K., Johannsson, M., Jeong, J., … Randall, F. (2020). What electrophysiology tells us about Alzheimer’s disease: A window into the synchronization and connectivity of brain neurons. Neurobiology of Aging, 85, 58–73. https://doi.org/10.1016/j.neurobiolaging.2019.09.008
Baker, C. T. (2023). Documenting the effects of noninvasive prefrontal pIR HEG neurofeedback in the treatment of common mental health problems. NeuroRegulation, 10(3), 207–218. https://doi.org/10.15540/nr.10.3.207
Bakhshayesh, A. R., Hänsch, S., Wyschkon, A., Rezai, M. J., & Esser, G. (2011). Neurofeedback in ADHD: A single-blind randomized controlled trial. European Child & Adolescent Psychiatry, 20(9), 481–491. https://doi.org/10.1007/s00787-011-0208-y
Başar, E., Başar-Eroglu, C., Karakaş, S., & Schürmann, M. (2001). Gamma, alpha, delta, and theta oscillations govern cognitive processes. International Journal of Psychophysiology, 39(2–3), 241–248. https://doi.org/10.1016/s0167-8760(00)00145-8
Bassett, D. S., & Sporns, O. (2017). Network neuroscience. Nature Neuroscience, 20(3), 353–364. https://doi.org/10.1038/nn.4502
Bastos, A. M., & Schoffelen, J.-M. (2016). A tutorial review of functional connectivity analysis methods and their interpretational pitfalls. Frontiers in Systems Neuroscience, 9, Article 175. https://doi.org/10.3389/fnsys.2015.00175
Bech, P. (2009). Fifty years with the Hamilton scales for anxiety and depression. Psychotherapy and Psychosomatics, 78(4), 202–211. https://doi.org/10.1159/000214441
Beck, A. T., Steer, R. A., & Brown, G. K. (1996). BDI-II, Beck Depression Inventory: Manual. Psychological Corporation.
Bench, C. J., Friston, K. J., Brown, R. G., Scott, L. C., Frackowiak, R. S. J., & Dolan, R. J. (1992). The anatomy of melancholia – Focal abnormalities of cerebral blood flow in major depression. Psychological Medicine, 22(3), 607–615. https://doi.org/10.1017/S003329170003806X
Bland, J. M., & Altman, D. G. (1996). Statistics notes: Transforming data. BMJ, 312(7033), 770–770. https://doi.org/10.1136/bmj.312.7033.770
Blum, S., Jacobsen, N. S. J., Bleichner, M. G., & Debener, S. (2019). A Riemannian modification of artifact subspace reconstruction for EEG artifact handling. Frontiers in Human Neuroscience, 13, Article 141. https://doi.org/10.3389/fnhum.2019.00141
Broadway, J. M., Holtzheimer, P. E., Hilimire, M. R., Parks, N. A., DeVylder, J. E., Mayberg, H. S., & Corballis, P. M. (2012). Frontal theta cordance predicts 6-month antidepressant response to subcallosal cingulate deep brain stimulation for treatment-resistant depression: A pilot study. Neuropsychopharmacology, 37(7), 1764–1772. https://doi.org/10.1038/npp.2012.23
Carmen, J. A. (2004). Passive infrared hemoencephalography: Four years and 100 migraines. Journal of Neurotherapy, 8(3), 23–51. https://doi.org/10.1300/J184v08n03_03
Carrobles Isabel, J. A. (2016). Bio/neurofeedback. Clínica y Salud, 27(3), 125–131. https://doi.org/10.1016/j.clysa.2016.09.003
Chiarenza, G. A. (2021). Quantitative EEG in childhood attention deficit hyperactivity disorder and learning disabilities. Clinical EEG and Neuroscience, 52(2), 144–155. https://doi.org/10.1177/1550059420962343
Coben, R., & Padolsky, I. (2007). Infrared imaging and neurofeedback: Initial reliability and validity. Journal of Neurotherapy, 11(3), 3–13. https://doi.org/10.1080/10874200802126100
Cohen, M. X. (2014). Analyzing neural time series data: Theory and practice. The MIT Press. https://doi.org/10.7551/mitpress/9609.001.0001
Collura, T. (2012). BrainAvatar: Integrated brain imaging, neurofeedback, and reference database system. NeuroConnections Summer, 31–36.
Cook, I. A., & Leuchter, A. F. (1996). Synaptic dysfunction in Alzheimer’s disease: Clinical assessment using quantitative EEG. Behavioural Brain Research, 78(1), 15–23. https://doi.org/10.1016/0166-4328(95)00214-6
Cook, I. A., Leuchter, A. F., Morgan, M., Witte, E., Stubbeman, W. F., Abrams, M., Rosenberg, S., and Uijtdehaage, S. H. J. (2002). Early changes in prefrontal activity characterize clinical responders to antidepressants. Neuropsychopharmacology, 27(1), 120–131. https://doi.org/10.1016/S0893-133X(02)00294-4
Dede, A. J. O., Xiao, W., Vaci, N., Cohen, M. X., & Milne, E. (2023). Lack of univariate, clinically-relevant biomarkers of autism in resting state EEG: A study of 776 participants. medRxiv. https://doi.org/10.1101/2023.05.21.23290300
Drevets, W. C., Price, J. L., & Furey, M. L. (2008). Brain structural and functional abnormalities in mood disorders: Implications for neurocircuitry models of depression. Brain Structure & Function, 213(1–2), 93–118. https://doi.org/10.1007/s00429-008-0189-x
Erguzel, T. T., Ozekes, S., Gultekin, S., Tarhan, N., Hizli Sayar, G., & Bayram, A. (2015). Neural network based response prediction of rTMS in major depressive disorder using QEEG cordance. Psychiatry Investigation, 12(1), 61–65. https://doi.org/10.4306/pi.2015.12.1.61
Faiman, I., Sparks, R., Winston, J. S., Brunnhuber, F., Ciulini, N., Young, A. H., & Shotbolt, P. (2023). Limited clinical validity of univariate resting-state EEG markers for classifying seizure disorders. Brain Communications, 5(6), Article fcad330. https://doi.org/10.1093/braincomms/fcad330
Friston, K. (2009). Causal modelling and brain connectivity in functional magnetic resonance imaging. PLoS Biology, 7(2), Article e1000033. https://doi.org/10.1371/journal.pbio.1000033
Gazzaniga, M. S. (2000). Cerebral specialization and interhemispheric communication: Does the corpus callosum enable the human condition? Brain, 123(7), 1293–1326. https://doi.org/10.1093/brain/123.7.1293
Gómez-Benito, J., Ruiz, C., & Guilera, G. (2011). A Spanish version of the Athens insomnia scale. Quality of Life Research, 20(6), 931–937. https://doi.org/10.1007/s11136-010-9827-x
Gramfort, A., Luessi, M., Larson, E., Engemann, D. A., Strohmeier, D., Brodbeck, C., Goj, R., Jas, M., Brooks, T., Parkkonen, L., & Hämäläinen, M. (2013). MEG and EEG data analysis with MNE-Python. Frontiers in Neuroscience, 7, Article 267. https://doi.org/10.3389/fnins.2013.00267
Grimm, S., Beck, J., Schuepbach, D., Hell, D., Boesiger, P., Bermpohl, F., Niehaus, L., Boeker, H., & Northoff, G. (2008). Imbalance between left and right dorsolateral prefrontal cortex in major depression is linked to negative emotional judgment: An fMRI study in severe major depressive disorder. Biological Psychiatry, 63(4), 369–376. https://doi.org/10.1016/j.biopsych.2007.05.033
Hamilton, M. (1959). The assessment of anxiety states by rating. British Journal of Medical Psychology, 32(1), 50–55. https://doi.org/10.1111/j.2044-8341.1959.tb00467.x
Hamilton, M. (1960). A rating scale for depression. Journal of Neurology, Neurosurgery & Psychiatry, 23(1), 56–62. https://doi.org/10.1136/jnnp.23.1.56
Hammer, B. U., Colbert, A. P., Brown, K. A., & Ilioi, E. C. (2011). Neurofeedback for insomnia: A pilot study of Z-Score SMR and individualized protocols. Applied Psychophysiology and Biofeedback, 36(4), 251–264. https://doi.org/10.1007/s10484-011-9165-y
Hammond, D. C. (2005). Neurofeedback treatment of depression and anxiety. Journal of Adult Development, 12(2–3), 131–137. https://doi.org/10.1007/s10804-005-7029-5
Harris, C. R., Millman, K. J., Van Der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., Van Kerkwijk, M. H., Brett, M., Haldane, A., Del Río, J. F., Wiebe, M., Peterson, P., … Oliphant, T. E. (2020). Array programming with NumPy. Nature, 585(7825), 357–362. https://doi.org/10.1038/s41586-020-2649-2
Hjorth, B. (1975). An on-line transformation of EEG scalp potentials into orthogonal source derivations. Electroencephalography and Clinical Neurophysiology, 39(5), 526–530. https://doi.org/10.1016/0013-4694(75)90056-5
Höller, Y., & Nardone, R. (2021). Quantitative EEG biomarkers for epilepsy and their relation to chemical biomarkers. In G. S. Makowsi (Ed.), Advances in clinical chemistry (Vol. 102, pp.271–336). Elsevier. https://doi.org/10.1016/bs.acc.2020.08.004
Hunter, A. M., Leuchter, A. F., Morgan, M. L., & Cook, I. A. (2006). Changes in brain function (quantitative EEG cordance) during placebo lead-in and treatment outcomes in clinical trials for major depression. The American Journal of Psychiatry, 163(8), 1426–1432. https://doi.org/10.1176/ajp.2006.163.8.1426
Hunter, A. M., Nghiem, T. X., Cook, I. A., Krantz, D. E., Minzenberg, M. J., & Leuchter, A. F. (2018). Change in quantitative EEG theta cordance as a potential predictor of repetitive transcranial magnetic stimulation clinical outcome in major depressive disorder. Clinical EEG and Neuroscience, 49(5), 306–315. https://doi.org/10.1177/1550059417746212
Jacobson, N. S., & Truax, P. (1991). Clinical significance: A statistical approach to defining meaningful change in psychotherapy research. Journal of Consulting and Clinical Psychology, 59(1), 12–19. https://doi.org/10.1037/0022-006X.59.1.12
Kappenman, E. S., & Luck, S. J. (2010). The effects of electrode impedance on data quality and statistical significance in ERP recordings. Psychophysiology, 47(5), 888–904. https://doi.org/10.1111/j.1469-8986.2010.01009.x
Kayiran, S., Dursun, E., Ermutlu, N., Dursun, N., & Karamürsel, S. (2007). Neurofeedback in fibromyalgia syndrome. Agri: Agri (Algoloji) Dernegi’nin Yayin Organidir = The Journal of the Turkish Society of Algology, 19(3), 47–53.
Kendall, P. C., Hollon, S. D., Beck, A. T., Hammen, C. L., & Ingram, R. E. (1987). Issues and recommendations regarding use of the Beck Depression Inventory. Cognitive Therapy and Research, 11(3), 289–299. https://doi.org/10.1007/BF01186280
Kothe, C. A., & Jung, T.-P. (2016). Artifact removal techniques with signal reconstruction (Patent No. US20160113587A1).
Leuchter, A. F., Uijtdehaage, S. H., Cook, I. A., O'Hara, R., & Mandelkern, M. (1999). Relationship between brain electrical activity and cortical perfusion in normal subjects. Psychiatry Research: Neuroimaging, 90(2), 125–140. https://doi.org/10.1016/S0925-4927(99)00006-2
Leuchter, A. F., Cook, I. A., Lufkin, R. B., Dunkin, J., Newton, T. F., Cummings, J. L., Mackey, J. K., & Walter, D. O. (1994). Cordance: A new method for assessment of cerebral perfusion and metabolism using quantitative electroencephalography. NeuroImage, 1(3), 208–219. https://doi.org/10.1006/nimg.1994.1006
Leuchter, A. F., Cook, I. A., Mena, I., Dunkin, J. J., Cummings, J. L., Newton, T. F., Migneco, O., Lufkin, R. B., Walter, D. O., & Lachenbruch, P. A. (1994). Assessment of cerebral perfusion using quantitative EEG cordance. Psychiatry Research: Neuroimaging, 55(3), 141–152. https://doi.org/10.1016/0925-4927(94)90022-1
Livint Popa, L., Dragos, H., Pantelemon, C., Verisezan Rosu, O., & Strilciuc, S. (2020). The role of quantitative EEG in the diagnosis of neuropsychiatric disorders. Journal of Medicine and Life, 13(1), 8–15. https://doi.org/10.25122/jml-2019-0085
Lobo, A., Chamorro, L., Luque, A., Dal-Ré, R., Badia, X., Baró, E. & Grupo de Validación en Español de Escalas Psicométricas (GVEEP) (2002). Validación de las versiones en español de la Montgomery-Asberg Depression Rating Scale y la Hamilton Anxiety Rating Scale para la evaluación de la depresión y de la ansiedad [Validation of the Spanish versions of the Montgomery-Asberg Depression and Hamilton Anxiety rating scales]. Medicina Clínica, 118(13), 493–499. https://doi.org/10.1016/S0025-7753(02)72429-9
Maier, W., Buller, R., Philipp, M., & Heuser, I. (1988). The Hamilton anxiety scale: Reliability, validity and sensitivity to change in anxiety and depressive disorders. Journal of Affective Disorders, 14(1), 61–68. https://doi.org/10.1016/0165-0327(88)90072-9
Marzbani, H., Marateb, H., & Mansourian, M. (2016). Methodological note: Neurofeedback: A comprehensive review on system design, methodology and clinical applications. Basic and Clinical Neuroscience Journal, 7(2), 143–158. https://doi.org/10.15412/J.BCN.03070208
Mayberg, H. S., Brannan, S. K., Tekell, J. L., Silva, J. A., Mahurin, R. K., McGinnis, S., & Jerabek, P. A. (2000). Regional metabolic effects of fluoxetine in major depression: Serial changes and relationship to clinical response. Biological Psychiatry, 48(8), 830–843. https://doi.org/10.1016/s0006-3223(00)01036-2
Mayberg, H. S., Lozano, A. M., Voon, V., McNeely, H. E., Seminowicz, D., Hamani, C., Schwalb, J. M., & Kennedy, S. H. (2005). Deep brain stimulation for treatment-resistant depression. Neuron, 45(5), 651–660. https://doi.org/10.1016/j.neuron.2005.02.014
McAleavey, A. A. (2024). When (not) to rely on the reliable change index: A critical appraisal and alternatives to consider in clinical psychology. Clinical Psychology: Science and Practice, 31(3), 351–366. https://doi.org/10.1037/cps0000203
McVoy, M., Lytle, S., Fulchiero, E., Aebi, M. E., Adeleye, O., & Sajatovic, M. (2019). A systematic review of quantitative EEG as a possible biomarker in child psychiatric disorders. Psychiatry Research, 279, 331–344. https://doi.org/10.1016/j.psychres.2019.07.004
Medaglia, J. D., Lynall, M.-E., & Bassett, D. S. (2015). Cognitive network neuroscience. Journal of Cognitive Neuroscience, 27(8), 1471–1491. https://doi.org/10.1162/jocn_a_00810
Michel, C. M., & Murray, M. M. (2012). Towards the utilization of EEG as a brain imaging tool. NeuroImage, 61(2), 371–385. https://doi.org/10.1016/j.neuroimage.2011.12.039
Niedermeyer, E., & Lopes da Silva, F. H. (2005). Electroencephalography: Basic principles, clinical applications, and related fields (5th ed.). Lippincott Williams & Wilkins.
Northoff, G., & Tumati, S. (2019). “Average is good, extremes are bad” – Non-linear inverted U-shaped relationship between neural mechanisms and functionality of mental features. Neuroscience & Biobehavioral Reviews, 104, 11–25. https://doi.org/10.1016/j.neubiorev.2019.06.030
Nunez, P. L., & Srinivasan, R. (2006). A theoretical basis for standing and traveling brain waves measured with human EEG with implications for an integrated consciousness. Clinical Neurophysiology, 117(11), 2424–2435. https://doi.org/10.1016/j.clinph.2006.06.754
Pérez-Elvira, R., Carrobles, J., López Bote, D., & Oltra-Cucarella, J. (2019). Efficacy of live z-score neurofeedback training for chronic insomnia: A single-case study. NeuroRegulation, 6(2), 93–101. https://doi.org/10.15540/nr.6.2.93
Pérez-Elvira, R., & Jiménez Gómez, A. (2020). sLORETA neurofeedback in fibromyalgia. Neuroscience Research Notes, 3(1), 1–10. https://doi.org/10.31117/neuroscirn.v3i1.40
Pérez-Elvira, R., Oltra-Cucarella, J., & Carrobles, J. (2020). Comparing live z-score training and theta/beta protocol to reduce theta-to-beta ratio: A pilot study. NeuroRegulation, 7(2), 58–63. https://doi.org/10.15540/nr.7.2.58
Pérez-Elvira, R., Oltra-Cucarella, J., & Carrobles, J. A. (2021). Effects of quantitative electroencephalogram normalization using 4-channel live z-score training neurofeedback for children with learning disabilities: Preliminary data. Behavioral Psychology/Psicología Conductual, 29(1), 191–206. https://doi.org/10.51668/bp.8321110n
Pokorny, L., Biermann, L., Breitinger, E., Jarczok, T. A., Wagner, D., Vöckel, J., & Bender, S. (2024). Young adults with anxiety disorders show reduced inhibition in the dorsolateral prefrontal cortex at higher trait anxiety levels: A TMS-EEG study. Depression and Anxiety, 2024, Article 2758522. https://doi.org/10.1155/2024/2758522
Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52(3), 1059–1069. https://doi.org/10.1016/j.neuroimage.2009.10.003
Sanz, J., & García-Vera, M. P. (2013). Rendimiento diagnóstico y estructura factorial del Inventario para la Depresión de Beck–Segunda edición (BDI-II) en pacientes españoles con trastornos psicológicos [Diagnostic performance and factorial structure of the Beck Depression Inventory–Second edition (BDI-II)]. Anales de Psicología, 29(1), 66–75. https://doi.org/10.6018/analesps.29.1.130532
Sanz, J., Perdigó, L. A., & Vázquez, C. (2003). Adaptación española del Inventario para la depresión de Beck-II (BDI-II): 2. Propiedades psicométricas en población general [The Spanish adaptation of Beck's Depression Inventory-ll (BDI-II): 2. Psychometric properties in the general population]. Clínica y Salud, 14(3), 249–280.
Sateia, M. J. (2014). International classification of sleep disorders-third edition. Chest, 146(5), 1387–1394. https://doi.org/10.1378/chest.14-0970
Schomer, D. L., & Lopes da Silva, F. H. (Eds.). (2017). Niedermeyer’s electroencephalography (Vol. 1). Oxford University Press. https://doi.org/10.1093/med/9780190228484.001.0001
Shahid, A., Wilkinson, K., Marcu, S., & Shapiro, C. M. (2011). Athens Insomnia Scale (AIS). In A. Shahid, K. Wilkinson, S. Marcu, & C. M. Shapiro (Eds.), STOP, THAT and one hundred other sleep scales (pp. 53–54). Springer New York. https://doi.org/10.1007/978-1-4419-9893-4_5
Sitaram, R., Ros, T., Stoeckel, L., Haller, S., Scharnowski, F., Lewis-Peacock, J., Weiskopf, N., Blefari, M. L., Rana, M., Oblak, E., Birbaumer, N., & Sulzer, J. (2017). Closed-loop brain training: The science of neurofeedback. Nature Reviews Neuroscience, 18(2), 86–100. https://doi.org/10.1038/nrn.2016.164
Soldatos, C. R., Dikeos, D. G., & Paparrigopoulos, T. J. (2000). Athens insomnia scale: Validation of an instrument based on ICD-10 criteria. Journal of Psychosomatic Research, 48(6), 555–560. https://doi.org/10.1016/S0022-3999(00)00095-7
Soldatos, C. R., Dikeos, D. G., & Paparrigopoulos, T. J. (2003). The diagnostic validity of the Athens insomnia scale. Journal of Psychosomatic Research, 55(3), 263–267. https://doi.org/10.1016/S0022-3999(02)00604-9
Storey, J. D. (2002). A direct approach to false discovery rates. Journal of the Royal Statistical Society Series B: Statistical Methodology, 64(3), 479–498. https://doi.org/10.1111/1467-9868.00346
Sugarman, M. A., Loree, A. M., Baltes, B. B., Grekin, E. R., & Kirsch, I. (2014). The efficacy of paroxetine and placebo in treating anxiety and depression: A meta-analysis of change on the Hamilton rating scales. PLoS ONE, 9(8), Article e106337. https://doi.org/10.1371/journal.pone.0106337
Sürmeli, T., & Ertem, A. (2011). Obsessive compulsive disorder and the efficacy of qEEG-guided neurofeedback treatment: A case series. Clinical EEG and Neuroscience, 42(3), 195–201. https://doi.org/10.1177/155005941104200310
Tas, C., Cebi, M., Tan, O., Hızlı-Sayar, G., Tarhan, N., & Brown, E. C. (2015). EEG power, cordance and coherence differences between unipolar and bipolar depression. Journal of Affective Disorders, 172, 184–190. https://doi.org/10.1016/j.jad.2014.10.001
Teplan, M. (2002). Fundamentals of EEG measurement. Measurement Science Review, 2(2), 1–11. https://www.measurement.sk/2002/S2/Teplan.pdf
Thatcher, R. W. (2016). Handbook of quantitative electroencephalography and EEG biofeedback (2nd ed.). Anipublishing.
Thatcher, R. W. (2021). NeuroGuide manual. Applied Neuroscience, Inc.
The Pandas Development Team. (2024). Pandas-dev/pandas: Pandas (Versión v2.2.3) [Software]. Zenodo. https://doi.org/10.5281/ZENODO.3509134
Thibodeau, R., Jorgensen, R. S., & Kim, S. (2006). Depression, anxiety, and resting frontal EEG asymmetry: A meta-analytic review. Journal of Abnormal Psychology, 115(4), 715–729. https://doi.org/10.1037/0021-843X.115.4.715
Thompson, E. (2015). Hamilton rating scale for anxiety (HAM-A). Occupational Medicine, 65(7), 601. https://doi.org/10.1093/occmed/kqv054
Videbech, P. (2000). PET measurements of brain glucose metabolism and blood flow in major depressive disorder: A critical review. Acta Psychiatrica Scandinavica, 101(1), 11–20. https://doi.org/10.1034/j.1600-0447.2000.101001011.x
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., Van Der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., … Vázquez-Baeza, Y. (2020). SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nature Methods, 17(3), 261–272. https://doi.org/10.1038/s41592-019-0686-2
Walker, J. (2005). Neurofeedback treatment of epilepsy. Child and Adolescent Psychiatric Clinics of North America, 14(1), 163–176. https://doi.org/10.1016/j.chc.2004.07.009
Wascher, E., Schneider, D., Gajewski, P. D., & Getzmann, S. (2024). Resting-state EEG data before and after cognitive activity across the adult lifespan and a 5-year follow-up [Dataset]. Openneuro. https://doi.org/10.18112/OPENNEURO.DS005385.V1.0.2
Wutzl, B., Leibnitz, K., & Murata, M. (2024). An analysis of the correlation between the asymmetry of different EEG-sensor locations in diverse frequency bands and short-term subjective well-being changes. Brain Sciences, 14(3), Article 267. https://doi.org/10.3390/brainsci14030267
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Ruben Perez-Elvira, Javier Oltra-Cucarella, María Agudo Juan, Raúl Juárez Vela, Alfonso Salgado Ruíz

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-BY) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).





