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Anbarian, Mehrdad, Basatnia, Ruhollah. (1405). Multifractal Complexity Analysis of Electroencephalography (EEG) Signals and Kinematic Dynamics During Walking. سامانه مدیریت نشریات علمی, 10(2), 13-24. doi: 10.22098/jast.2025.18618.1444
Mehrdad Anbarian; Ruhollah Basatnia. "Multifractal Complexity Analysis of Electroencephalography (EEG) Signals and Kinematic Dynamics During Walking". سامانه مدیریت نشریات علمی, 10, 2, 1405, 13-24. doi: 10.22098/jast.2025.18618.1444
Anbarian, Mehrdad, Basatnia, Ruhollah. (1405). 'Multifractal Complexity Analysis of Electroencephalography (EEG) Signals and Kinematic Dynamics During Walking', سامانه مدیریت نشریات علمی, 10(2), pp. 13-24. doi: 10.22098/jast.2025.18618.1444
Anbarian, Mehrdad, Basatnia, Ruhollah. Multifractal Complexity Analysis of Electroencephalography (EEG) Signals and Kinematic Dynamics During Walking. سامانه مدیریت نشریات علمی, 1405; 10(2): 13-24. doi: 10.22098/jast.2025.18618.1444

Multifractal Complexity Analysis of Electroencephalography (EEG) Signals and Kinematic Dynamics During Walking

Journal of Advanced Sport Technology
دوره 10، شماره 2 - شماره پیاپی 25، مرداد 2026، صفحه 13-24    اصل مقاله (1.32 M)
نوع مقاله: Original research papers
شناسه دیجیتال (DOI): 10.22098/jast.2025.18618.1444
نویسندگان
Mehrdad Anbarian* ؛ Ruhollah Basatnia
Department of Sports Biomechanics, Faculty of Sports Sciences, Bu-Ali Sina University, Hamedan, Iran.
چکیده
Walking, as a complex motor activity, requires precise coordination within the neuromuscular system. This study aimed to analyze the multiscale complexity of electroencephalography (EEG) signals and kinematic data during walking to investigate brain-lower limb interactions. Thirteen male participants walked on a treadmill under controlled conditions, during which EEG signals and kinematic data were recorded. Multifractal complexity analysis using the multifractal detrended fluctuation analysis (MF-DFA) method was applied to the data to extract the Hurst exponent as a complexity index for dynamic features of movements in displacement dimensions along three axes (X, Y, Z) and cortical activity in motor brain regions (C3 and C4) during walking. Results indicated that the generalized Hurst exponent H(q) in the C3 and C4 regions was similar and exhibited a significant positive correlation with the same parameter in the dynamics of the contralateral limb, supporting the principle of interhemispheric control. Segmental analysis of the thigh, shank, and foot revealed substantial dynamic symmetry between the right and left sides. The multifractal patterns of the segments demonstrated significant differences, with the highest H(q) in the shank and the lowest in the thigh. Strong intra-system coordination among segments suggests an integrated organization in motor control. These findings confirm neuromuscular symmetry and coordination, provide utility for assessing motor disorders and designing rehabilitation protocols, and underscore the importance of multiscale analysis in elucidating complex brain-body interactions, with potential applications in neuroscience, biomechanics, and rehabilitation research.
کلیدواژه‌ها
Walking؛ EEG signals؛ kinematic data؛ multifractal analysis؛ Hurst exponent
مراجع
References

  1. Busa MA, van Emmerik REA. Multiscale entropy: A tool for understanding the complexity of postural control. J Sport Health Sci 2016;5:44–51. DOI: 10.1016/j.jshs.2016.01.018
  2. Shine JM, Matar E, Ward PB, Bolitho SJ, Pearson M, Naismith SL, et al. Differential Neural Activation Patterns in Patients with Parkinson’s Disease and Freezing of Gait in Response to Concurrent Cognitive and Motor Load. PLoS One 2013;8:e52602. DOI: 1371/journal.pone.0052602
  3. Stergiou N, Stergiou N, Decker LM, Decker LM. Human movement variability, nonlinear dynamics, and pathology: Is there a connection? Hum Mov Sci 2011. DOI: 10.1016/j.humov.2011.06.002
  4. Zorick T, Mandelkern MA. Multifractal Detrended Fluctuation Analysis of Human EEG: Preliminary Investigation and Comparison with the Wavelet Transform Modulus Maxima Technique. PLoS One 2013;8:e68360. DOI: 10.1371/journal.pone.0068360
  5. Racz FS, Stylianou O, Mukli P, Eke A. Multifractal and entropy analysis of resting-state electroencephalography reveals spatial organization in local dynamic functional connectivity. Sci Rep 2019;9. DOI: 10.1038/s41598-019-49726-5
  6. Ihlen EAF. Introduction to multifractal detrended fluctuation analysis in Matlab. Front Physiol 2012;3 JUN:23948. DOI: 10.3389/fphys.2012.00141
  7. Delignières D, Torre K, Bernard PL. Transition from Persistent to Anti-Persistent Correlations in Postural Sway Indicates Velocity-Based Control. PLoS Comput Biol 2011;7:e1001089. DOI: 10.1371/journal.pcbi.1001089
  8. Goldberger AL, Amaral LAN, Hausdorff JM, Ivanov PC, Peng CK, Stanley HE. Fractal dynamics in physiology: Alterations with disease and aging. Proc Natl Acad Sci U S A 2002;99:2466. DOI: 10.1073/pnas.012579499
  9. Gaurav G, Anand RS, Kumar V. EEG based cognitive task classification using multifractal detrended fluctuation analysis. Cogn Neurodyn 2021;15:999–1013. DOI: 10.1007/s11571-021-09684-z
  10. Zorick T, Landers J, Leuchter A, Mandelkern MA. EEG multifractal analysis correlates with cognitive testing scores and clinical staging in mild cognitive impairment. Journal of Clinical Neuroscience 2020;76:195–200. DOI: 10.1016/j.jocn.2020.04.003
  11. Racz FS, Stylianou O, Mukli P, Eke A. Multifractal and entropy analysis of resting-state electroencephalography reveals spatial organization in local dynamic functional connectivity. Sci Rep 2019;9. DOI: 10.1038/s41598-019-49726-5
  12. Pavlov AN, Maksimenko VA, Runnova AE, Khramova M V., Pisarchik AN. Multifractal analysis of real and imaginary movements: EEG study 2018;10717:35. DOI: 10.1117/12.2311482
  13. West BJ, Scafetta N. A Multifractal Dynamical Model of Human Gait. Fractals in Biology and Medicine 2005:131–40. DOI: 10.1007/3-7643-7412-8_12
  14. Faini A, Arsac LM, Deschodt-Arsac V, Castiglioni P. Multifractal Multiscale Analysis of Human Movements during Cognitive Tasks. Entropy 2024;26. DOI: 10.3390/E26020148
  15. Lencioni T, Carpinella I, Rabuffetti M, Marzegan A, Ferrarin M. Human kinematic, kinetic and EMG data during different walking and stair ascending and descending tasks. Sci Data 2019;6:1–10. DOI: 10.1038/S41597-019-0323-z
  16. Geng S, Zhou W, Yuan Q, Cai D, Zeng Y. EEG non-linear feature extraction using correlation dimension and Hurst exponent. Neurol Res 2011;33:908–12. DOI: 10.1179/1743132811Y.0000000041
  17. Abdul-Latif AA, Cosic I, Kumar DK, Polus B, Pah N, Djuwari D. EEG coherence changes between right and left motor cortical areas during voluntary muscular contraction. Australas Phys Eng Sci Med 2004;27:11–5. DOI: 10.1007/BF03178882
  18. Neuper C, Schlögl A, Pfurtscheller G. Enhancement of left-right sensorimotor EEG differences during feedback- regulated motor imagery. Journal of Clinical Neurophysiology 1999;16:373–82. DOI: 10.1097/00004691-199907000-00010
  19. Blythe DAJ, Haufe S, Müller KR, Nikulin V V. The effect of linear mixing in the EEG on Hurst exponent estimation. Neuroimage 2014;99:377–87. DOI: 10.1016/J.NEUROIMAGE.2014.05.041
  20. Phinyomark A, Larracy R, Scheme E. Fractal Analysis of Human Gait Variability via Stride Interval Time Series. Front Physiol 2020;11:333. DOI: 10.3389/FPHYS.2020.00333
  21. Warlop T, Detrembleur C, Bollens B, Stoquart G, Crevecoeur F, Jeanjean A, et al. Degradation of gait autocorrelation as precocious marker of fall risk in Parkinson’s disease. Ann Phys Rehabil Med 2015;58:e71. DOI: 10.1016/J.REHAB.2015.07.175
  22. Hausdorff JM. Gait dynamics in Parkinson’s disease: Common and distinct behavior among stride length, gait variability, and fractal-like scaling. Chaos 2009;19:026113. DOI: 10.1063/1.3147408
  23. Decker LM, Cignetti F, Stergiou N. Complexity and human gait. Revista Andaluza de Medicina del Deporte. 2010;3(1):2-12. https://digitalcommons.unomaha.edu/biomechanicsarticles/91
  24. Günther M, Bartsch RP, Miron-Shahar Y, Hassin-Baer S, Inzelberg R, Kurths J, et al. Coupling between leg muscle activation and EEG during normal walking, intentional stops, and freezing of gait in Parkinson’s disease. Front Physiol 2019;10:870. DOI: 10.3389/FPHYS.2019.00870
  25. Maidan I, Patashov D, Shustak S, Fahoum F, Gazit E, Shapiro B, et al. A new approach to quantifying the EEG during walking: Initial evidence of gait related potentials and their changes with aging and dual tasking. Exp Gerontol 2019;126. DOI: 10.1016/J.EXGER.2019.110709
  26. Aldea R, Tarniceriu D. Estimating the hurst exponent in motor imagery-based brain computer interface. 2013 7th Conference on Speech Technology and Human - Computer Dialogue, SpeD 2013 2013. DOI: 10.1109/SPED.2013.6682656
  27. Gupta A, Kumar D, Chakraborti A. Hurst Exponent as a New Ingredient to Parametric Feature Set for Mental Task Classification. Advances in Intelligent Systems and Computing 2018;701:129–37. DOI: 10.1007/978-981-10-7563-6_14
  28. Hallett M, DelRosso LM, Elble R, Ferri R, Horak FB, Lehericy S, et al. Evaluation of movement and brain activity. Clinical Neurophysiology 2021;132:2608–38. DOI: 10.1016/J.CLINPH.2021.04.023
  29. Phinyomark A, Larracy R, Scheme E. Fractal Analysis of Human Gait Variability via Stride Interval Time Series. Front Physiol 2020;11:333. DOI: 10.3389/FPHYS.2020.00333
  30. Tesio L, Rota V. The Motion of Body Center of Mass During Walking: A Review Oriented to Clinical Applications. Front Neurol 2019;10. DOI: 10.3389/FNEUR.2019.00999
  31. Ji Q, Qian Z, Ren L, Ren L. Simulation Analysis of Impulsive Ankle Push-Off on the Walking Speed of a Planar Biped Robot. Front Bioeng Biotechnol 2021;8:621560. DOI: 10.3389/FBIOE.2020.621560
  32. Hausdorff JM. Gait dynamics in Parkinson’s disease: Common and distinct behavior among stride length, gait variability, and fractal-like scaling. Chaos 2009;19:026113. DOI: 10.1063/1.3147408
  33. Salazar-Varas R, Costa A, Úbeda A, Iáñez E, Azorín JM. Changes in brain activity due to the sudden apparition of an obstacle during gait. International IEEE/EMBS Conference on Neural Engineering, NER 2015;2015-July:110–3. DOI: 10.1109/NER.2015.7146572
  34. Azizi T. On the fractal geometry of gait dynamics in different neuro-degenerative diseases. Physics in Medicine 2022. DOI: 10.1016/j.phmed.2022.100050
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