1. Segalowitz S. J., Santesso D. L., Jetha M. K. Electrophysiological changes during adolescence: A review. Brain and Cognition, 2010, 72(1): 86–100. https://doi.org/10.1016/j.bandc.2009.10.003
2. Zhang H., Zhou Q. Q., Chen H., Hu X. Q., Li W. G., Bai Y., Han J. X., Wang Y., Liang Z. H., Chen D., Cong F. Y., Yan J. Q., Li X. L. The applied principles of EEG analysis methods in neuroscience and clinical neurology. Military Medical Research, 2023, 10(1). https://doi.org/10.1186/s40779-023-00502-7
3. Müller-Putz G. R. Electroencephalography. Handbook of Clinical Neurology, 2020, 168: 249–262. https://doi.org/10.1016/B978-0-444-63934-9.00018-4
4. Seneviratne U., D'Souza W. J. Ambulatory EEG. Handbook of clinical neurology, 2019, 160: 161–170. https://doi.org/10.1016/B978-0-444-64032-1.00010-2
5. Romagnoli S., Franchi F., Ricci Z. Processed EEG monitoring for anesthesia and intensive care practice. Minerva Anestesiologica, 2019, 85(11): 1219–1230. https://doi.org/10.23736/S0375-9393.19.13478-5
6. Omejc N., Rojc B., Battaglini P. P., Marusic U. Review of the therapeutic neurofeedback method using electroencephalography: EEG Neurofeedback. Bosnian Journal of Basic Medical Sciences, 2019, 19(3): 213–220. https://doi.org/10.17305/bjbms.2018.3785
7. McSweeney M., Morales S., Valadez E. A., Buzzell G. A., Fox N. A. Longitudinal age- and sex-related change in background aperiodic activity during early adolescence. Developmental Cognitive Neuroscience, 2021, 52. https://doi.org/10.1016/j.dcn.2021.101035
8. McKeon S. D., Perica M. I., Parr A. C., Calabro F. J., Foran W., Hetherington H., Moon C.-H., Luna B. Aperiodic EEG and 7T MRSI evidence for maturation of E/I balance supporting the development of working memory through adolescence. bioRxiv, 2023. https://doi.org/10.1101/2023.09.06.556453
9. Cragg L., Kovacevic N., McIntosh A. R., Poulsen C., Martinu K., Leonard G., Paus T. Maturation of EEG power spectra in early adolescence: A longitudinal study. Developmental Science, 2011, 14(5): 935–943. https://doi.org/10.1111/j.1467-7687.2010.01031.x
10. Soroko S. I., Shemyakina N. V., Nagornova Z. V., Bekshaev S. S. Longitudinal study of EEG frequency maturation and power changes in children on the Russian North. International Journal of Developmental Neuroscience, 2014, 38: 127–137. https://doi.org/10.1016/j.ijdevneu.2014.08.012
11. Jackson A. F., Bolger D. J. The neurophysiological bases of EEG and EEG measurement: A review for the rest of us. Psychophysiology, 2014, 51(11): 1061–1071. https://doi.org/10.1111/psyp.12283
12. Chan H. L., Kuo P. C., Cheng C. Y., Chen Y. S. Challenges and future perspectives on electroencephalogram-based biometrics in person recognition. Frontiers in Neuroinformatics, 2018, 12. https://doi.org/10.3389/fninf.2018.00066
13. Zhong W., An X., Di Y., Zhang L., Ming D. Review on identity feature extraction methods based on electroencephalogram signals. Journal of Biomedical Engineering, 2021, 38(6): 1203–1210. https://doi.org/10.7507/1001-5515.202102057
14. Ahmadieh H., Ghassemi F. Assessing the effects of Alzheimer disease on EEG signals using the entropy measure: A meta-analysis. Basic and Clinical Neuroscience, 2022, 13(2): 153–164. https://doi.org/10.32598/bcn.2021.1144.3
15. Smailovic U., Jelic V. Neurophysiological markers of Alzheimer's disease: Quantitative EEG approach. Neurology and Therapy, 2019, 8: 37–55. https://doi.org/10.1007/s40120-019-00169-0
16. Bangert M., Altenmüller E. O. Mapping perception to action in piano practice: A longitudinal DC-EEG study. BMC Neuroscience, 2003, 4. https://doi.org/10.1186/1471-2202-4-26
17. Shaw S. B., Levy Y., Mizzi A., Herman G., McKinnon M. C., Heisz J. J., Becker S. Combined aerobic exercise and neurofeedback lead to improved task-relevant intrinsic network synchrony. Frontiers in Human Neuroscience, 2022, 16. https://doi.org/10.3389/fnhum.2022.838614
18. Kavčič A., Demšar J., Georgiev D., Bon J., Soltirovska-Šalamon A. Age-related changes and sex-related differences of functional brain networks in childhood: A high-density EEG study. Clinical Neurophysiology, 2023, 150: 216–226. https://doi.org/10.1016/j.clinph.2023.03.357
19. Kroupi E., JH Jones E., Oakley B., Buitelaar J., Charman T., Loth E., Murphy D., Soria-Frisch A. Age-related differences in delta-beta phase-amplitude coupling in autistic individuals. Clinical Neurophysiology, 2024, 167: 74–83. https://doi.org/10.1016/j.clinph.2024.08.010
20. Raudenbush S. W. Comparing personal trajectories and drawing causal inferences from longitudinal data. Annual Review of Psychology, 2001, 52: 501–525. https://doi.org/10.1146/annurev.psych.52.1.501
21. Boland J., Telesca D., Sugar C., Jeste S., Goldbeck C., Senturk D. A study of longitudinal trends in time-frequency transformations of EEG data during a learning experiment. Computational Statistics & Data Analysis, 2022, 167. https://doi.org/10.1016/j.csda.2021.107367
22. Ricci A., He F., Calhoun S. L., Fang J., Vgontzas A. N., Liao D., Bixler E. O., Younes M., Fernandez-Mendoza J. Sex and pubertal differences in the maturational trajectories of sleep spindles in the transition from childhood to adolescence: A population-based study. eNeuro, 2021, 8(4). https://doi.org/10.1523/eneuro.0257-21.2021
23. Dawson G., Rieder A. D., Johnson M. H. Prediction of autism in infants: Progress and challenges. The Lancet Neurology, 2023, 22(3): 244–254. https://doi.org/10.1016/S1474-4422(22)00407-0
24. Giannakopoulos P., Missonnier P., Gold G., Michon A. Electrophysiological markers of rapid cognitive decline in mild cognitive impairment. Frontiers of Neurology and Neuroscience, 2009, 24: 39–46. https://doi.org/10.1159/000197898
25. Jeong J. EEG dynamics in patients with Alzheimer's disease. Clinical Neurophysiology, 2004, 115(7): 1490–1505. https://doi.org/10.1016/j.clinph.2004.01.001
26. Dijkstra F., de Volder I., Viaene M., Cras P., Crosiers D. Polysomnographic predictors of sleep, motor, and cognitive dysfunction progression in Parkinson’s disease. Current Neurology and Neuroscience Reports, 2022, 22(10): 657–674. https://doi.org/10.1007/s11910-022-01226-2
27. Arheix-Parras S., Glize B., Guehl D., Python G. Electrophysiological changes in patients with post-stroke aphasia: A systematic review. Brain Topography, 2023, 36: 135–171. https://doi.org/10.1007/s10548-023-00941-4
28. Watts D., Pulice R. F., Reilly J., Brunoni A. R., Kapczinski F., Passos I. C. Predicting treatment response using EEG in major depressive disorder: A machine-learning meta-analysis. Translational Psychiatry, 2022, 12(1). https://doi.org/10.1038/s41398-022-02064-z
29. Jacobs R. H., Orr J. L., Gowins J. R., Forbes E. E., Langenecker S. A. Biomarkers of intergenerational risk for depression: A review of mechanisms in longitudinal high-risk (LHR) studies. Journal of Affective Disorders, 2015, 175: 494–506. https://doi.org/10.1016/j.jad.2015.01.038
30. Bel-Bahar T. S., Khan A. A., Shaik R. B., Parvaz M. A. A scoping review of electroencephalographic (EEG) markers for tracking neurophysiological changes and predicting outcomes in substance use disorder treatment. Frontiers in Human Neuroscience, 2022, 16. https://doi.org/10.3389/fnhum.2022.995534
31. Abbott C. C., Gallegos P., Rediske N., Lemke N. T., Quinn D. K. A review of longitudinal electroconvulsive therapy: Neuroimaging investigations. Journal of Geriatric Psychiatry and Neurology, 2014, 27(1): 33–46. https://doi.org/10.1177/0891988713516542
32. Feinberg I., Campbell I. G. Sleep EEG changes during adolescence: An index of a fundamental brain reorganization. Brain and Cognition, 2010, 72(1): 56–65. https://doi.org/10.1016/j.bandc.2009.09.008
33. Schaworonkow N., Voytek B. Longitudinal changes in aperiodic and periodic activity in electrophysiological recordings in the first seven months of life. Developmental Cognitive Neuroscience, 2021, 47. https://doi.org/10.1016/j.dcn.2020.100895
34. Rico-Picó J., Moyano S., Conejero Á., Hoyo Á., Ballesteros-Duperón M. Á., Rueda M. R. Early development of electrophysiological activity: Contribution of periodic and aperiodic components of the EEG signal. Psychophysiology, 2023, 60(11). https://doi.org/10.1111/psyp.14360
35. Wilkinson C. L., Yankowitz L. D., Chao J. Y., Gutiérrez R., Rhoades J. L., Shinnar S., Purdon P. L., Nelson C. A. Developmental trajectories of EEG aperiodic and periodic components in children 2–44 months of age. Nature Communications, 2024, 15(1). https://doi.org/10.1038/s41467-024-50204-4
36. Howarth G. Z., Fettig N. B., Curby T. W., Bell M. A. Frontal electroencephalogram asymmetry and temperament across infancy and early childhood: An exploration of stability and bidirectional relations. Child Development, 2016, 87(2): 465–476. https://doi.org/10.1111/cdev.12466
37. Jenni O. G., Borbély A. A., Achermann P. Development of the nocturnal sleep electroencephalogram in human infants. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 2004, 286(3): R528–R538. https://doi.org/10.1152/ajpregu.00503.2003
38. Beaugrand M., Jaramillo V., Markovic A., Huber R., Kohler M., Schoch S. F., Kurth S. Lack of association between behavioral development and simplified topographical markers of the sleep EEG in infancy. Neurobiology of Sleep and Circadian Rhythms, 2023, 15. https://doi.org/10.1016/j.nbscr.2023.100098
39. LeBourgeois M. K., Dean D. C., Deoni S. C. L., Kohler M., Kurth S. A simple sleep EEG marker in childhood predicts brain myelin 3.5 years later. NeuroImage, 2019, 199: 342–350. https://doi.org/10.1016/j.neuroimage.2019.05.072
40. Attaheri A., Choisdealbha Á. N., Di Liberto G. M., Rocha S., Brusini P., Mead N., Olawole-Scott H., Boutris P., Gibbon S., Williams I., Grey C., Flanagan S., Goswami U. Delta- and theta-band cortical tracking and phase-amplitude coupling to sung speech by infants. NeuroImage, 2022, 247. https://doi.org/10.1016/j.neuroimage.2021.118698
41. Van der Velde B., White T., Kemner C. The emergence of a theta social brain network during infancy. NeuroImage, 2021, 240. https://doi.org/10.1016/j.neuroimage.2021.118298
42. Lustenberger C., Mouthon A. L., Tesler N., Kurth S., Ringli M., Buchmann A., Jenni O. G., Huber R. Developmental trajectories of EEG sleep slow wave activity as a marker for motor skill development during adolescence: A pilot study. Developmental Psychobiology, 2017, 59(1): 5–14. https://doi.org/10.1002/dev.21446
43. Dünki R. M., Schmid G. B., Stassen H. H. Intraindividual specificity and stability of human EEG: Comparing a linear vs a nonlinear approach. Methods of Information in Medicine, 2000, 39(1): 78–82. https://doi.org/10.1055/s-0038-1634249
44. Maiorana E., Campisi P. Longitudinal evaluation of EEG-based biometric recognition. IEEE Transactions on Information Forensics and Security, 2018, 13(5): 1123–1138. https://doi.org/10.1109/tifs.2017.2778010
45. Vandenbosch M. M. L. J. Z., Van ’t Ent D., Boomsma D. I., Anokhin A. P., Smit D. J. A. EEG-based age-prediction models as stable and heritable indicators of brain maturational level in children and adolescents. Human Brain Mapping, 2019, 40(6): 1919–1926. https://doi.org/10.1002/hbm.24501
46. Al Zoubi O., Ki Wong C., Kuplicki R. T., Yeh H., Mayeli A., Refai H., Paulus M., Bodurka J. Predicting age from brain EEG signals – A machine learning approach. Frontiers in Aging Neuroscience, 2018, 10. https://doi.org/10.3389/fnagi.2018.00184
47. Müller B. C. N., Kühn-Popp N., Meinhardt J., Sodian B., Paulus M. Long-term stability in children's frontal EEG alpha asymmetry between 14-months and 83-months. International Journal of Developmental Neuroscience, 2015, 41(1): 110–114. https://doi.org/10.1016/j.ijdevneu.2015.01.002
48. Markovic A., Achermann P., Rusterholz T., Tarokh L. Heritability of sleep EEG topography in adolescence: Results from a longitudinal twin study. Scientific Reports, 2018, 8(1): 3–13. https://doi.org/10.1038/s41598-018-25590-7
49. Feinberg I., Campbell I. G. Longitudinal sleep EEG trajectories indicate complex patterns of adolescent brain maturation. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 2013, 304(4): R296–R303. https://doi.org/10.1152/ajpregu.00422.2012
50. Vakorin V. A., McIntosh A. R., Mišić B., Krakovska O., Poulsen C., Martinu K., Paus T. Exploring age-related changes in dynamical non-stationarity in electroencephalographic signals during early adolescence. PLOS ONE, 2013, 8(3). https://doi.org/10.1371/journal.pone.0057217
51. Gmehlin D., Thomas C., Weisbrod M., Walther S., Pfüller U., Resch F., Oelkers-Ax R. Individual analysis of EEG background activity within school age: Impact of age and sex within a longitudinal data set. International Journal of Developmental Neuroscience, 2011, 29(2): 163–170. https://doi.org/10.1016/j.ijdevneu.2010.11.005
52. Campbell I. G., Darchia N., Higgins L. M., Dykan I. V., Davis N. M., de Bie E., Feinberg I. Adolescent changes in homeostatic regulation of EEG activity in the delta and theta frequency bands during NREM sleep. Sleep, 2011, 34(1): 83–91. https://doi.org/10.1093/sleep/34.1.83
53. Campbell I. G., Van Dongen H. P. A., Gainer M., Karmouta E., Feinberg I. Differential and interacting effects of age and sleep restriction on daytime sleepiness and vigilance in adolescence: A longitudinal study. Sleep, 2018, 41(12): 1–8. https://doi.org/10.1093/sleep/zsy177
54. Baker F. C., Turlington S. R., Colrain I. Developmental changes in the sleep electroencephalogram of adolescent boys and girls. Journal of Sleep Research, 2011, 21(1): 59–67. https://doi.org/10.1111/j.1365-2869.2011.00930.x
55. Olbrich E., Rusterholz T., LeBourgeois M. K., Achermann P. Developmental changes in sleep oscillations during early childhood. Neural Plasticity, 2017, (1): 1–12. https://doi.org/10.1155/2017/6160959
56. Ricci A., He F., Fang J., Calhoun S. L., Vgontzas A. N., Liao D., Younes M., Bixler E. O., Fernandez-Mendoza J. Maturational trajectories of non-rapid eye movement slow wave activity and odds ratio product in a population-based sample of youth. Sleep Medicine, 2021, 83: 271–279. https://doi.org/10.1016/j.sleep.2021.05.002
57. Kiss O., Goldstone A., de Zambotti M., Yüksel D., Hasler B. P., Franzen P. L., Brown S. A., De Bellis M. D., Nagel B. J., Nooner K. B., Tapert S. F., Colrain I. M., Clark D. B., Baker F. C. Effects of emerging alcohol use on developmental trajectories of functional sleep measures in adolescents. Sleep, 2023, 46(9): 1–14. https://doi.org/10.1093/sleep/zsad113
58. Campbell I. G., Feinberg I. Longitudinal trajectories of non-rapid eye movement delta and theta EEG as indicators of adolescent brain maturation. Proceedings of the National Academy of Sciences, 2009, 106(13): 5177–5180. https://doi.org/10.1073/pnas.0812947106
59. Campbell I. G., Higgins L. M., Trinidad J. M., Richardson P., Feinberg I. The increase in longitudinally measured sleepiness across adolescence is related to the maturational decline in low-frequency EEG power. Sleep, 2007, 30(12): 1677–1687. https://doi.org/10.1093/sleep/30.12.1677
60. Feinberg I., Higgins L. M., Khaw W. Y., Campbell I. G. The adolescent decline of NREM delta, an indicator of brain maturation, is linked to age and sex but not to pubertal stage. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 2006, 291(6): R1724–R1729. https://doi.org/10.1152/ajpregu.00293.2006
61. Kurth S., Riedner B. A., Dean D. C., O'Muircheartaigh J., Huber R., Jenni O. G., Deoni S. C. L., LeBourgeois M. K. Traveling slow oscillations during sleep: A marker of brain connectivity in childhood. Sleep, 2017, 40(9): 1–10. https://doi.org/10.1093/sleep/zsx121
62. Campbell I. G., Kraus A. M., Burright C. S., Feinberg I. Restricting time in bed in early adolescence reduces both NREM and REM sleep but does not increase slow-wave EEG. Sleep, 2016, 39(9): 1663–1670. https://doi.org/10.5665/sleep.6088
63. McClain I. J., Lustenberger C., Achermann P., Lassonde J. M., Kurth S., LeBourgeois M. K. Developmental changes in sleep spindle characteristics and sigma power across early childhood. Neural Plasticity, 2016, (1): 1–9. https://doi.org/10.1155/2016/3670951
64. Zhang Z. Y., Campbell I. G., Dhayagude P., Espino H. C., Feinberg I. Longitudinal analysis of sleep spindle maturation from childhood through late adolescence. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 2021, 41(19): 4253–4261. https://doi.org/10.1523/JNEUROSCI.2370-20.2021
65. Campbell I. G., Feinberg I. Maturational patterns of sigma frequency power across childhood and adolescence: A longitudinal study. Sleep, 2016, 39(1): 193–201. https://doi.org/10.5665/sleep.5346
66. Campbell I. G., Darchia N., Khaw W. Y., Higgins L. M., Feinberg I. Sleep EEG evidence of sex differences in adolescent brain maturation. Sleep, 2005, 28(5): 637–643. https://doi.org/10.1093/sleep/28.5.637
67. Van Baal G. C. M., Boomsma D. I., De Geus E. J. C. Longitudinal genetic analysis of EEG coherence in young twins. Behavior Genetics, 2001, 31(6): 637–651. https://doi.org/10.1023/a:1013357714500
68. Ríos-López P., Molinaro N., Bourguignon M., Lallier M. Development of neural oscillatory activity in response to speech in children from 4 to 6 years old. Developmental Science, 2020, 23(6): 1–16. https://doi.org/10.1111/desc.12947
69. Tarokh L., Carskadon M. A., Achermann P. Developmental changes in brain connectivity assessed using the sleep EEG. Neuroscience, 2010, 171(2): 622–634. https://doi.org/10.1016/j.neuroscience.2010.08.071
70. Knyazev G. G., Savostyanov A. N., Bocharov A. V., Tamozhnikov S. S., Kozlova E. A., Leto I. V., Slobodskaya H. R. Cross-frequency coupling in developmental perspective. Frontiers in Human Neuroscience, 2019, 13. https://doi.org/10.3389/fnhum.2019.00158
71. Knyazev G. G., Savostyanov A. N., Bocharov A. V., Slobodskaya H. R., Bairova N. B., Tamozhnikov S. S., Stepanova V. V. Effortful control and resting state networks: A longitudinal EEG study. Neuroscience, 2017, 346: 365–381. https://doi.org/10.1016/j.neuroscience.2017.01.031
72. Gao C., Scullin M. K. Age-related longitudinal trajectories in NREM and REM spectral power. Sleep, 2020, 43(1): A130–A131. https://doi.org/10.1093/sleep/zsaa056.341
73. Saggar M., Zanesco A. P., King B. G., Bridwell D. A., MacLean K. A., Aichele S. R., Jacobs T. L., Wallace B. A., Saron C. D., Miikkulainen R. Mean-field thalamocortical modeling of longitudinal EEG acquired during intensive meditation training. NeuroImage, 2015, 114: 88–104. https://doi.org/10.1016/j.neuroimage.2015.03.073
74. Saggar M., King B. G., Zanesco A. P., MacLean K. A., Aichele S. R., Jacobs T. L., Bridwell D. A., Shaver P. R., Rosenberg E. L., Sahdra B. K., Ferrer E., Tang A. C., Mangun G. R., Wallace B. A., Miikkulainen R., Saron C. D. Intensive training induces longitudinal changes in meditation state-related EEG oscillatory activity. Frontiers in Human Neuroscience, 2012, 6. https://doi.org/10.3389/fnhum.2012.00256
75. Skwara A. C., King B. G., Zanesco A. P., Saron C. D. Shifting baselines: Longitudinal reductions in EEG beta band power characterize resting brain activity with intensive meditation. Mindfulness, 2022, 13(10): 2488–2506. https://doi.org/10.1007/s12671-022-01974-9
76. Gao J., Fan J., Wu B. W. Y., Zhang Z., Chang C., Hung Y.-S., Sik H. H. Entrainment of chaotic activities in brain and heart during MBSR mindfulness training. Neuroscience Letters, 2016, 616: 218–223. https://doi.org/10.1016/j.neulet.2016.01.001
77. An A., Hoang H., Trang L., Vo Q., Tran L., Le T., Le A., McCormick A., Du Old K., Williams N. S., Mackellar G., Nguyen E., Luong T., Nguyen V., Nguyen K., Ha H. Investigating the effect of mindfulness-based stress reduction on stress level and brain activity of college students. IBRO Neuroscience Reports, 2022, 12: 399–410. https://doi.org/10.1016/j.ibneur.2022.05.004
78. Zanesco A. P., Skwara A. C., King B. G., Powers C., Wineberg K., Saron C. D. Meditation training modulates brain electric microstates and felt states of awareness. Human Brain Mapping, 2021, 42(10): 3228–3252. https://doi.org/10.1002/hbm.25430
79. Dziego C. A., Zanesco A. P., Bornkessel-Schlesewsky I., Schlesewsky M., Stanley E. A., Jha A. P. Mindfulness training in high-demand cohorts alters resting-state electroencephalography: An exploratory investigation of individual alpha frequency, aperiodic 1/f activity, and microstates. Biological Psychiatry Global Open Science, 2024, 4(6). https://doi.org/10.1016/j.bpsgos.2024.100383
80. Moore A., Gruber T., Derose J., Malinowski P. Regular, brief mindfulness meditation practice improves electrophysiological markers of attentional control. Frontiers in Human Neuroscience, 2012, 6. https://doi.org/10.3389/fnhum.2012.00018
81. Nyhus E., Engel W. A., Pitfield T. D., Vakkur I. M. W. Increases in theta oscillatory activity during episodic memory retrieval following mindfulness meditation training. Frontiers in Human Neuroscience, 2019, 13. https://doi.org/10.3389/fnhum.2019.00311
82. Carpentier S. M., Moreno S., McIntosh A. R. Short-term music training enhances complex, distributed neural communication during music and linguistic tasks. Journal of Cognitive Neuroscience, 2016, 28(10): 1603–1612. https://doi.org/10.1162/jocn_a_00988
83. Khalil A., Musacchia G., Iversen J. R. It takes two: Interpersonal neural synchrony is increased after musical interaction. Brain Sciences, 2022, 12(3). https://doi.org/10.3390/brainsci12030409
84. Moussiopoulou J., Pross B., Handrack M., Keeser D., Pogarell O., Halle M., Falkai P., Scherr J., Hasan A., Roeh A. The influence of marathon running on resting-state EEG activity: A longitudinal observational study. European Journal of Applied Physiology, 2024, 124(4): 1311–1321. https://doi.org/10.1007/s00421-023-05356-4
85. Campbell I. G., Cruz-Basilio A., Darchia N., Zhang Z. Y., Feinberg I. Effects of sleep restriction on the sleep electroencephalogram of adolescents. Sleep, 2021, 44(6): 1–9. https://doi.org/10.1093/sleep/zsaa280
86. Campbell I. G., Grimm K. J., de Bie E., Feinberg I. Sex, puberty, and the timing of sleep EEG-measured adolescent brain maturation. Proceedings of the National Academy of Sciences, 2012, 109(15): 5740–5743. https://doi.org/10.1073/pnas.1120860109
87. Hatzinger M., Brand S., Perren S., Von Wyl A., Stadelmann S., Von Klitzing K., Holsboer-Trachsler E. In pre-school children, sleep objectively assessed via sleep-EEGs remains stable over 12 months and is related to psychological functioning, but not to cortisol secretion. Journal of Psychiatric Research, 2013, 47(11): 1809–1814. https://doi.org/10.1016/j.jpsychires.2013.08.007
88. Yousof S., Ibrahim D., El-Baz A., Osama A., El-Wazir Y. Gender difference in the effect of examination stress on brain oscillations during memory tasks. Suez Canal University Medical Journal, 2014, 17(1): 21–28. http://dx.doi.org/10.21608/scumj.2014.45588
89. Do T. T. N., Wang Y. K., Lin C. T. Increase in brain effective connectivity in multitasking but not in a high-fatigue state. IEEE Transactions on Cognitive and Developmental Systems, 2020, 1(1): 566–574. https://doi.org/10.1109/TCDS.2020.2990898
90. Harper J., Malone S. M., Iacono W. G. Testing the effects of adolescent alcohol use on adult conflict-related theta dynamics. Clinical Neurophysiology, 2017, 128(11): 2358–2368. https://doi.org/10.1016/j.clinph.2017.08.019
91. Brandes-Aitken A., Pini N., Weatherhead M., Brito N. H. Maternal hair cortisol predicts periodic and aperiodic infant frontal EEG activity longitudinally across infancy. Developmental Psychobiology, 2023, 65(5): 1–11. https://doi.org/10.1002/dev.22393
92. Stamoulis C., Vanderwert R. E., Zeanah C. H., Fox N. A., Nelson C. A. Neuronal networks in the developing brain are adversely modulated by early psychosocial neglect. Journal of Neurophysiology, 2017, 118(4): 2275–2288. https://doi.org/10.1152/jn.00014.2017
93. Schwartz L., Hayut O., Levy J., Gordon I., Feldman R. Sensitive infant care tunes a frontotemporal interbrain network in adolescence. Scientific Reports, 2024, 14(1). https://doi.org/10.1038/s41598-024-73630-2
94. Jones K. T., Johnson E. L., Gazzaley A., Zanto T. P. Structural and functional network mechanisms of rescuing cognitive control in aging. NeuroImage, 2022, 262. https://doi.org/10.1016/j.neuroimage.2022.119547
95. Jones K. T., Peterson D. J., Blacker K. J., Berryhill M. E. Frontoparietal neurostimulation modulates working memory training benefits and oscillatory synchronization. Brain Research, 2017, 1667: 28–40. https://doi.org/10.1016/j.brainres.2017.05.005
96. Pugin F., Metz A. J., Wolf M., Achermann P., Jenni O. G., Huber R. Local increase of sleep slow wave activity after three weeks of working memory training in children and adolescents. Sleep, 2015, 38(4): 607–614. https://doi.org/10.5665/sleep.4580
97. Hannesdóttir D. K., Doxie J., Bell M. A., Ollendick T. H., Wolfe C. D. A longitudinal study of emotion regulation and anxiety in middle childhood: Associations with frontal EEG asymmetry in early childhood. Developmental psychobiology, 2010, 52(2): 197–204. https://doi.org/10.1002/dev.20425
98. Poole K. L., Santesso D. L., Van Lieshout R. J., Schmidt L. A. Trajectories of frontal brain activity and socio-emotional development in children. Developmental Psychobiology, 2018, 60(4): 353–363. https://doi.org/10.1002/dev.21620
99. Harrewijn A., Buzzell G. A., Debnath R., Leibenluft E., Pine D. S., Fox N. A. Frontal alpha asymmetry moderates the relations between behavioral inhibition and social-effect ERN. Biological Psychology, 2019, 141: 10–16. https://doi.org/10.1016/j.biopsycho.2018.12.014
100. Hassan R., Schmidt L. A. Longitudinal investigation of shyness and physiological vulnerability: Moderating influences of attention biases to threat and safety. Developmental Psychobiology, 2021, 63(7): 1–13. https://doi.org/10.1002/dev.22180
101. Goldstein B. L., Shankman S. A., Kujawa A., Torpey-Newman D. C., Dyson M. W., Olino T. M., Klein D. N. Positive and negative emotionality at age 3 predicts change in frontal EEG asymmetry across early childhood. Journal of Abnormal Child Psychology, 2019, 47(2): 209–219. https://doi.org/10.1007/s10802-018-0433-7
102. McManis M. H., Kagan J., Snidman N. C., Woodward S. A. EEG asymmetry, power, and temperament in children. Developmental Psychobiology, 2002, 41(2): 169–177. https://doi.org/10.1002/dev.10053
103. He J., Degnan K. A., McDermott J. M., Henderson H. A., Hane A. A., Xu Q., Fox N. A. Anger and approach motivation in infancy: Relations to early childhood inhibitory control and behavior problems. Infancy, 2010, 15(3): 246–269. https://doi.org/10.1111/j.1532-7078.2009.00017.x
104. Broomell A. P. R., Savla J., Bell M. A. Infant electroencephalogram coherence and toddler inhibition are associated with social responsiveness at age 4. Infancy, 2019, 24(1): 43–56. https://doi.org/10.1111/infa.12273
105. Poole K. L., Schmidt L. A. Frontal brain delta-beta correlation, salivary cortisol, and social anxiety in children. The Journal of Child Psychology and Psychiatry, 2019, 60(6): 646–654. https://doi.org/10.1111/jcpp.13016
106. Myruski S., Bagrodia R., Dennis-Tiwary T. Delta-beta correlation predicts adaptive child emotion regulation concurrently and two years later. Biological Psychology, 2022, 167. https://doi.org/10.1016/j.biopsycho.2021.108225
107. Sacks D. D., Schwenn P. E., Boyes A., Mills L., Driver C., Gatt J. M., Lagopoulos J., Hermens D. F. Longitudinal associations between resting-state, interregional theta-beta phase-amplitude coupling, psychological distress, and wellbeing in 12–15-year-old adolescents. Cerebral Cortex, 2023, 33(12): 8066–8074. https://doi.org/10.1093/cercor/bhad099
108. Niv S., Ashrafulla S., Tuvblad C., Joshi A., Raine A., Leahy R., Baker L. A. Childhood EEG frontal alpha power as a predictor of adolescent antisocial behavior: A twin heritability study. Biological Psychology, 2015, 105: 72–76. https://doi.org/10.1016/j.biopsycho.2014.11.010
109. Tenke C. E., Kayser J., Svob C., Miller L., Alvarenga J. E., Abraham K., Warner V., Wickramaratne P., Weissman M. M., Bruder G. E. Association of posterior EEG alpha with prioritization of religion or spirituality: A replication and extension at 20-year follow-up. Biological Psychology, 2017, 124: 79–86. https://doi.org/10.1016/j.biopsycho.2017.01.005
110. Eisenhut L., Sadeghi-Bahmani D., Ngo V. T., Mikoteit T., Brühl A. B., Stadler C., Dürsteler K. M., Hatzinger M., Brand S. The origins of the dark-hyperactivity and negative peer relationships, an objectively lower sleep efficiency, and a longer sleep onset latency at age five were associated with callous-unemotional traits and low empathy at age 14. Journal of Clinical Medicine, 2023, 12(6): 1–18. https://doi.org/10.3390/jcm12062248
111. Mundy P., Card J., Fox N. EEG correlates of the development of infant joint attention skills. Developmental Psychobiology, 2000, 36(4): 325–338. https://doi.org/10.1002/(SICI)1098-2302(200005)36:4<325::AID-DEV7>3.0.CO;2-F
112. Mundy P., Fox N., Card J. EEG coherence, joint attention and language development in the second year. Developmental Science, 2003, 6(1): 48–54. https://doi.org/10.1111/1467-7687.00253
113. Henderson L. M., Yoder P. J., Yale M. E., McDuffie A. Getting the point: Electrophysiological correlates of protodeclarative pointing. International Journal of Developmental Neuroscience, 2002, 20(3-5): 449–458. https://doi.org/10.1016/s0736-5748(02)00038-2
114. Brandes-Aitken A., Metser M., Braren S. H., Vogel S. C., Brito N. H. Neurophysiology of sustained attention in early infancy: Investigating longitudinal relations with recognition memory outcomes. Infant Behavior and Development, 2023, 70. https://doi.org/10.1016/j.infbeh.2022.101807
115. Cuevas K., Bell M. A., Marcovitch S., Calkins S. D. Electroencephalogram and heart rate measures of working memory at 5 and 10 months of age. Developmental Psychology, 2012, 48(4): 907–917. https://doi.org/10.1037/a0026448
116. Bell M. A., Wolfe C. D. Changes in brain functioning from infancy to early childhood: Evidence from EEG power and coherence working memory tasks. Developmental Neuropsychology, 2007, 31(1): 21–38. https://doi.org/10.1207/s15326942dn3101_2
117. Ríos-López P., Molinaro N., Bourguignon M., Lallier M. Right-hemisphere coherence to speech at pre-reading stages predicts reading performance one year later. Journal of Cognitive Psychology, 2021, 34(2): 179–193. https://doi.org/10.1080/20445911.2021.1986514
118. Schiavone G., Linkenkaer-Hansen K., Maurits N. M., Plakas A., Maassen B. A. M., Mansvelder H. D., Van der Leij A., Van Zuijen T. L. Preliteracy signatures of poor-reading abilities in resting-state EEG. Frontiers in human neuroscience, 2014, 8. https://doi.org/10.3389/fnhum.2014.00735
119. Lyakso E. E., Frolova O. V., Grigorev A. S. Infant vocalizations at the first year of life predict speech development at 2–7 years: Longitudinal study. Psychology, 2014, 5(12): 1433–1445. https://doi.org/10.4236/psych.2014.512154
120. Bowman L. C., Dodell-Feder D., Saxe R., Sabbagh M. A. Continuity in the neural system supporting children's theory of mind development: Longitudinal links between task-independent EEG and task-dependent fMRI. Developmental cognitive neuroscience, 2019, 40. https://doi.org/10.1016/j.dcn.2019.100705
121. Finley A. J., Angus D. J., Knight E. L., Van Reekum C. M., Lachman M. E., Davidson R. J., Schaefer S. M. Resting EEG periodic and aperiodic components predict cognitive decline over 10 years. The Journal of Neuroscience, 2024, 44(13): 1–12. https://doi.org/10.1523/JNEUROSCI.1332-23.2024
122. Brooks H., Mirjalili M., Wang W., Kumar S., Goodman M. S., Zomorrodi R., Blumberger D. M., Bowie C. R., Daskalakis Z. J., Fischer C. E., Flint A. J., Herrmann N., Lanctôt K. L., Mah L., Mulsant B. H., Pollock B. G., Voineskos A. N., Rajji T. K. Assessing the longitudinal relationship between theta-gamma coupling and working memory performance in older adults. Cerebral Cortex, 2022, 32(8): 1653–1667. https://doi.org/10.1093/cercor/bhab295
123. Zhang L., Gan J. Q., Wang H. Neurocognitive mechanisms of mathematical giftedness: A literature review. Applied Neuropsychology: Child, 2017, 6(1): 79–94. https://doi.org/10.1080/21622965.2015.1119692
124. Gómez C. M., Barriga-Paulino C. I., Rodríguez-Martínez E. I., Rojas-Benjumea M. Á., Arjona A., Gómez-González J. The neurophysiology of working memory development: From childhood to adolescence and young adulthood. Reviews in the Neurosciences, 2018, 29(3): 261–282. https://doi.org/10.1515/revneuro-2017-0073
