References
1.Craik, K.J. “Theory of the human operator in control systems 1: I. The operator as an engineering system”, Br. J. Psychol. Gen. Sect., 38(2), pp. 56-61 (1947). https://doi.org/10.1111/j.2044-8295.1947.tb01141.x
2.Tustin, A. “The nature of the operator's response in manual control, and its implications for controller design”, J. Inst. Elec. Eng., Part IIA: Automatic Regulators and Servo Mechanisms, 94(2), pp. 190-206 (1947). https://doi.org/10.1049/ji-2a.1947.0025
3.Susilaradeya, D., Xu, W., Hall, T.M., et al. “Extrinsic and intrinsic dynamics in movement intermittency”, Elife, 8, e40145 (2019).  https://doi.org/10.7554/eLife.40145
4.Stark, L., Neurological Control Systems: Studies in Bioengineering, Springer, New York, NY (2012).  https://doi.org/10.1007/978-1-4684-0706-8 
5.Sakaguchi, Y., Tanaka, M., and Inoue, Y. “Adaptive intermittent control: A computational model explaining motor intermittency observed in human behavior”, Neural Networks, 67, pp. 92-109 (2015). https://doi.org/10.1016/j.neunet.2015.03.012
6.Nema, S., Kowalczyk, P., and Loram, I. “Wavelet-frequency analysis for the detection of discontinuities in switched system models of human balance”, Hum. Mov. Sci., 51, pp. 27-40 (2017). https://doi.org/10.1016/j.humov.2016.08.002
7.Gollee, H., Gawthrop, P.J., Lakie, M., et al. “Visuo‐manual tracking: Does intermittent control with aperiodic sampling explain linear power and non-linear remnant without sensorimotor noise?”, J. Physiol., 595(21), pp. 6751-6770 (2017).  https://doi.org/10.1113/jp274288
8.Choi, W., Lee, J., Yanagihara, N., et al. “Development of a quantitative evaluation system for visuo-motor control in three-dimensional virtual reality space”, Sci. Rep., 8, 13439 (2018).  https://doi.org/10.1038/s41598-018-31758-y
9.Sakaguchi, Y. “Intermittent brain motor control observed in continuous tracking task”, In Advances in Cognitive Neurodynamics (III), pp. 461-468 (2013). https://doi.org/10.1007/978-94-007-4792-0_62
10.Nowshiravan Rahatabad, F., Fallah, A., and Jafari, A.H. “A study of chaotic phenomena in human-like reaching movements”, Int. J. Bifurcat. Chaos, 21(11), pp. 3293-3303 (2011). https://doi.org/10.1142/S0218127411030532
11.Josiński, H., Grabiec, P., Pawlyta, M., et al. “Analysis of chaotic behaviors in gait of the elderly using the CAREN extended system”, 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom), pp. 1-6 (2018). https://doi.org/10.1109/HealthCom.2018.8531131
12.Lee, J., Guo, Y., Ravikumar, V., et al. “Towards the development of nonlinear approaches to discriminate AF from NSR using a single-lead ECG”, Entropy, 22(5), 531 (2020).   https://doi.org/10.3390/e22050531 
13.Miao, T., Shimizu, T., Makabe, H., et al. “Chaos in ear plethysmograms: Tracking experiment and a model”, IEEE International Conference on Systems, Man and Cybernetics, pp. 2982-2987 (2008).   https://doi.org/10.1109/ICSMC.2008.4811752 
14.Takagi, A., Furuta, R., Saetia, S., et al. “Behavioral and physiological correlates of kinetically tracking a chaotic target”, PLoS One, 15(9), e0239471 (2020). https://doi.org/10.1371/journal.pone.0239471
15.Dotov, D. and Froese, T. “Entraining chaotic dynamics: a novel movement sonification paradigm could promote generalization”, Hum. Mov. Sci., 61, pp. 27-41 (2018). https://doi.org/10.1016/j.humov.2018.06.016
16.Stepp, N. “Anticipation in feedback-delayed manual tracking of a chaotic oscillator”, Exp. Brain Res., 198, pp. 521-525 (2009). https://doi.org/10.1007/s00221-009-1940-0
17.Baran, V., Zus, M., Bonasera, A., et al. “Quantifying the folding mechanism in chaotic dynamics”, Rom. J. Phys., 60(9), pp. 1263-1277 (2015).
18.Ma, T., Ouellette, N.T., and Bollt, E.M. “Stretching and folding in finite time”, Chaos: Interdiscipl. J. Nonlinear Sci., 26, 023112 (2016). https://doi.org/10.1063/1.4941256
19.Ser-Giacomi, E., Rossi, V., López, C., et al. “Flow networks: A characterization of geophysical fluid transport”, Chaos: Interdiscipl. J. Nonlinear Sci., 25(3), 036404 (2015).     https://doi.org/10.1063/1.4908231
20.Huang, Y., Dehkordy, F.M., Li, Y., et al. “Enhancing anaerobic fermentation performance through eccentrically stirred mixing: Experimental and modeling methodology”, Chem. Eng. J., 334, pp. 1383-1391 (2018). https://doi.org/10.1016/j.cej.2017.11.088
21.Mostefa, T., Eddine, A.D., Tayeb, N.T., et al. “Kinematic properties of a twisted double planetary chaotic mixer: A three-dimensional numerical investigation”, Micromachines, 13(9), 1545 (2022). https://doi.org/10.3390/mi13091545
22.Christov, I.C., Lueptow, R.M., and Ottino, J.M. “Stretching and folding versus cutting and shuffling: An illustrated perspective on mixing and deformations of continua”, Am. J. Phys., 79(4), pp. 359-367 (2011). https://doi.org/10.1119/1.3533213
23.Souzy, M., Lhuissier, H., Méheust, Y., et al. “Velocity distributions, dispersion and stretching in three-dimensional porous media”, J. Fluid Mech., 891, A16 (2020). https://doi.org/10.1017/jfm.2020.113
24.Ubeyli, E.D. and Guler, İ. “Statistics over Lyapunov exponents for feature extraction: Electroencephalographic changes detection case”, International Journal of Psychological and Behavioral Sciences, 1(2), pp. 134-137 (2007). https://doi.org/10.5281/zenodo.1062899
25.Bonasera, A., Bucolo, M., Fortuna, L., et al. “Experimental evaluation of the d∞ parameter to characterize chaotic dynamics”, AIP Conf. Proc., 676(1), pp. 355-362 (2003). https://doi.org/10.1063/1.1612233
26.Ahmad, R., Farooqi, A., Zhang, J., et al. “Analysis of transport and mixing phenomenon to invariant manifolds using LCS and KAM theory approach in unsteady dynamical systems”, IEEE Access, 8, pp. 141057-141065 (2020).https://doi.org/10.1109/ACCESS.2020.3011569
27.Perez, G.M.P., Vidale, P.L., Klingaman, N.P., et al. “Atmospheric convergence zones stemming from large-scale mixing”, Weather Clim. Dynam. Discuss, 2(2), pp. 475-488 (2021).  https://doi.org/10.5194/wcd-2-475-2021 
28.Suresh, K., Prasad, A., and Thamilmaran, K. “Birth of strange nonchaotic attractors through formation and merging of bubbles in a quasiperiodically forced Chuaʼs oscillator”, Phys. Lett. A, 377(8), pp. 612-621 (2013).https://doi.org/10.1016/j.physleta.2012.12.026
29.Younes, E., Moguen, Y., El Omari, K., et al. “Experimental study of chaotic flow and mixing of Newtonian fluid in a rotating arc-wall mixer”, Int. J. Heat Mass Transf., 187, 122459 (2022). https://doi.org/10.1016/j.ijheatmasstransfer.2021.122459
30.Rössler, O.E. and Letellier, C., Chaos: The World of Nonperiodic Oscillations, Springer Nature (2020). https://doi.org/10.1007/978-3-030-44305-4
31.Chen, S., Hao, M., Shang, J., et al. “Numerical analysis of modified micromixers with staggered E-shape mixing units”, Chem. Eng. Process, 179, 109087 (2022). https://doi.org/10.1016/j.cep.2022.109087
32.Clemente-López, D., Tlelo-Cuautle, E., de la Fraga, L.-G., et al. “Poincaré maps for detecting chaos in fractional-order systems with hidden attractors for its Kaplan-Yorke dimension optimization”, AIMS Math., 7(4), pp. 5871-5894 (2022).    https://doi.org/10.3934/math.2022326 
33.Xie, J., Wang, Y., and Tang, B. “Chaotic dynamics of string around the Bardeen-AdS black holes surrounded by quintessence dark energy”, Physics of the Dark Universe, 40, 101184 (2023). https://doi.org/10.1016/j.dark.2023.101184
34.Jafari, S., Hashemi Golpayegani, S.M.R., and Jafari, A.H. “A novel noise reduction method based on geometrical properties of continuous chaotic signals”, Sci. Iran., 19(6), pp. 1837-1842 (2012).   https://doi.org/10.1016/j.scient.2012.10.032 
35.Molaie, M., Jafari, S., Moradi, M.H., et al. “A chaotic viewpoint on noise reduction from respiratory sounds”, Biomed. Signal Process. Control, 10, pp. 245-249 (2014).https://doi.org/10.1016/j.bspc.2013.10.009
36.Yang, Y., Liu, X., Wu, J., et al. “SimPer: Simple self-supervised learning of periodic targets”, arXiv: 2210.03115 (2022).     https://doi.org/10.48550/arXiv.2210.03115 
37.Yang, J., Zhang, J., Settle, C., et al. “Learning periodic tasks from human demonstrations”, arXiv:2109.14078(2022). https://doi.org/10.48550/arXiv.2109.14078
38.Guo, Q., Feng, W., Zhou, C., et al. “Learning dynamic siamese network for visual object tracking”, IEEE International Conference on Computer Vision (ICCV), pp. 1781-1789 (2017). https://doi.org/10.1109/ICCV.2017.196 
39.Makovski, T., Vazquez, G.A., and Jiang, Y.V. “Visual learning in multiple-object tracking”, PLoS One, 3(5), e2228 (2008).https://doi.org/10.1371/journal.pone.0002228
40.Mathew, J., Eusebio, A., and Danion, F. “Limited contribution of primary motor cortex in eye-hand coordination: A TMS study”, J. Neurosci., 37(40), pp. 9730-9740 (2017). https://doi.org/10.1523/jneurosci.0564-17.2017 
41.Bank, P.J.M., Dobbe, L.R.M., Meskers, C.G.M., et al. “Manipulation of visual information affects control strategy during a visuomotor tracking task”, Behav. Brain Res., 329, pp. 205-214 (2017). https://doi.org/10.1016/j.bbr.2017.04.056
42.Maiello, G., Kwon, M., and Bex, P.J. “Three-dimensional binocular eye–hand coordination in normal vision and with simulated visual impairment”, Exp. Brain Res., 236, pp. 691-709 (2018). https://doi.org/10.1007/s00221-017-5160-8
43.Parker, M.G., Willett, A.B.S., Tyson, S.F., et al. “A systematic evaluation of the evidence for perceptual control theory in tracking studies”, Neurosci. & Biobehav. Rev., 112, pp. 616-633 (2020). https://doi.org/10.1016/j.neubiorev.2020.02.030
44.Hilborn, R.C. Chaos and Nonlinear Dynamics: An Introduction for Scientists and Engineers, 2nd Edition, Oxford University Press (2000).https://doi.org/10.1093/acprof:oso/9780198507239.001.0001
45.Patterson, J.R., Brown, L.E., Wagstaff, D.A., et al. “Limb position drift results from misalignment of proprioceptive and visual maps”, J. Neurosci., 346, pp. 382-394 (2017). https://doi.org/10.1016/j.neuroscience.2017.01.040
46.Guigon, E., Chafik, O., Jarrasse, N., et al. “Experimental and theoretical study of velocity fluctuations during slow movements in humans”, J. of Neurophysiology, 121(2), pp. 715-727 (2019).  https://doi.org/10.1152/jn.00576.2018
47.Takens, F. Detecting Strange Attractors in Turbulence, In Dynamical Systems and Turbulence, Warwick 1980, pp. 366-381 (1981). https://doi.org/10.1007/BFb0091924
48.Liebert, W. and Schuster, H.G. “Proper choice of the time delay for the analysis of chaotic time series”, Phys. Lett. A, 142(2-3), pp. 107-111(1989). https://doi.org/10.1016/0375-9601(89)90169-2 
49.Kennel, M.B., Brown, R., and Abarbanel, H.D. “Determining embedding dimension for phase-space reconstruction using a geometrical construction”, Phys. Rev. A, 45, 3403 (1992). https://doi.org/10.1103/PhysRevA.45.3403 
50.de Pedro-Carracedo, J., Fuentes-Jimenez, D., Ugena, A.M., et al. “Phase space reconstruction from a biological time series: A photoplethysmographic signal case study”, Appl. Sci., 10(4), 1430 (2020).https://doi.org/10.3390/app10041430
51.Higuchi, T. “Approach to an irregular time series on the basis of the fractal theory”, Physica D, 31(2), pp. 277-283 (1988). https://doi.org/10.1016/0167-2789(88)90081-4
52.Katz, M.J. “Fractals and the analysis of waveforms”, Comput. Biol. Med., 18(3), pp. 145-156 (1988).https://doi.org/10.1016/0010-4825(88)90041-8
53.Petrosian, A. “Kolmogorov complexity of finite sequences and recognition of different preictal EEG patterns”, Proc. Eighth IEEE Symp. Computer-Based Med. Syst., pp. 212-217 (1995).   https://doi.org/10.1109/CBMS.1995.465426 
54.Kesić, S. and Spasić, S.Z. “Application of Higuchi's fractal dimension from basic to clinical neurophysiology: A review”, Comput. Methods Programs Biomed., 133, pp. 55-70 (2016). https://doi.org/10.1016/j.cmpb.2016.05.014
55.Yazdi-Ravandi, S., Arezooji, D.M., Matinnia, N., et al. “Complexity of information processing in obsessive-compulsive disorder based on fractal analysis of EEG signal”, EXCLI J., 20, pp. 642-654 (2021). https://doi.org/10.17179/excli2020-2783 
56.Sharanya, S. and Arjunan, S.P. “Fractal dimension techniques for analysis of Cardiac Autonomic Neuropathy (CAN)”, Biomedical Engineering: Applications, Basis and Communications, 35(03), 2350003 (2023). https://doi.org/10.4015/S1016237223500035
57.Vivekanandhan, G., Mehrabbeik, M., Rajagopal, K., et al. “Higuchi fractal dimension is a unique indicator of working memory content represented in spiking activity of visual neurons in extrastriate cortex”, Math. Biosci. Eng., 20(2), pp. 3749-3767 (2023). https://doi.org/10.3934/mbe.2023176
58.Sharma, K., Dash, A., and Kumar, D. “Investigating the effect of EEG channel selection on inter-subject emotion classification”, 13th Int. Conf. Cloud Comput. Data Sci. Eng., pp. 312-316 (2023). https://doi.org/10.1109/Confluence56041.2023.10048851
59.Amiri, M., Aghaeinia, H., and Amindavar, H.R. “Automatic epileptic seizure detection in EEG signals using sparse common spatial pattern and adaptive short-time Fourier transform-based synchrosqueezing transform”, Biomed. Signal Process. Control, 79(1), 104022 (2023). https://doi.org/10.1016/j.bspc.2022.104022
60.Negahbani, E., Amirfattahi, R., Ahmadi, B., et al. “Electroencephalogram fractal dimension as a measure of depth of anesthesia”, 3rd Int. Conf. Inf. Commun. Technol. Theory Appl., pp. 1-5 (2008).  https://doi.org/10.1109/ICTTA.2008.4530055 
61.Minkowski, L., Mai, K.V., and Gurve, D. “Feature extraction to identify depression and anxiety based on EEG”, 43rd Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC), pp. 6322-6325 (2021). https://doi.org/10.1109/embc46164.2021.9630821
62.Alim, A. and Imtiaz, M.H. “Automatic identification of children with ADHD from EEG brain waves”, Signals, 4(1), pp. 193-205 (2023). https://doi.org/10.3390/signals4010010
63.de Miras, J.R., Ibáñez-Molina, A.J., Soriano, M.F., et al. “Schizophrenia classification using machine learning on resting state EEG signal”, Biomed. Signal Process. Control, 79(2), 104233 (2023).   https://doi.org/10.1016/j.bspc.2022.104233 
64.Wanliss, J. and Wanliss, G.E. “Efficient calculation of fractal properties via the Higuchi method”, Nonlinear Dyn., 109, pp. 2893-2904 (2022). https://doi.org/10.1007/s11071-022-07353-2
65.Wang, B., Liu, X., Yu, B., et al. “An improved WiFi positioning method based on fingerprint clustering and signal weighted Euclidean distance”, Sensors, 19(10), 2300 (2019). https://doi.org/10.3390/s19102300 
66.Kuznetsov, N.V., Alexeeva, T.A., and Leonov, G.A. “Invariance of Lyapunov exponents and Lyapunov dimension for regular and irregular linearizations”, Nonlinear Dyn., 85, pp. 195-201 (2016). https://doi.org/10.1007/s11071-016-2678-4
67.Kantz, H. “A robust method to estimate the maximal Lyapunov exponent of a time series”, Phys. Lett. A, 185(1), pp. 77-87 (1994). https://doi.org/10.1016/0375-9601(94)90991-1 
68.Rampichini, S., Vieira, T.M., Castiglioni, P., et al. “Complexity analysis of surface electromyography for assessing the myoelectric manifestation of muscle fatigue: A review”, Entropy, 22(5), 529 (2020). https://doi.org/10.3390/e22050529
69.Lau, Z.J., Pham, T., Chen, S.H.A., et al. “Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations”, Eur. J. Neurosci., 56(7), pp. 5047-5069 (2022).     https://doi.org/10.1111/ejn.15800 
70.Grebogi, C., Ott, E., and Yorke, J.A. “Critical exponent of chaotic transients in nonlinear dynamical systems”, Phys. Rev. Lett., 57, 1284 (1986). https://doi.org/10.1103/PhysRevLett.57.1284
71.Hirata, Y., Oda, A.H., Motono, C., et al. “Imputation-free reconstructions of three-dimensional chromosome architectures in human diploid single-cells using allele-specified contacts”, Sci. Rep., 12, 11757 (2022). https://doi.org/10.1038/s41598-022-15038-4
72.Spasić, S. “Surrogate data test for nonlinearity of the rat cerebellar electrocorticogram in the model of brain injury”, Signal Process., 90(12), pp. 3015-3025 (2010). https://doi.org/10.1016/j.sigpro.2010.04.005 
73.Ahmad, S., Ullah, A., and Akgül, A. “Investigating the complex behaviour of multi-scroll chaotic system with Caputo fractal-fractional operator”, Chaos, Solitons & Fractals, 146, 110900 (2021). https://doi.org/10.1016/j.chaos.2021.110900
74.Miall, R.C., Weir, D., and Stein, J. “Intermittency in human manual tracking tasks”, J. Mot. Behav., 25(1), pp. 53-63 (1993).  https://doi.org/10.1080/00222895.1993.9941639
75.Huang, C.-T. and Hwang, I.-S. “Eye-hand synergy and intermittent behaviors during target-directed tracking with visual and non-visual information”, PLoS One, 7(12), e51417 (2012). https://doi.org/10.1371/journal.pone.0051417
76.Rashidi, S., Fallah, A., and Towhidkhah, F. “Nonlinear analysis of dynamic signature”, Indian J. Phys., 87(12), pp. 1251-1261 (2013). https://doi.org/10.1007/s12648-013-0358-5
77.Fegni Ndam, E.O., Goubault, E., Moyen-Sylvestre, B., et al. “What are the best indicators of myoelectric manifestation of fatigue?”, medRxiv, 23286583 (2023). https://doi.org/10.1101/2023.03.02.23286583
78.Mayor, D., Steffert, T., Datseris, G., et al. “Complexity and Entropy in Physiological Signals (CEPS): Resonance breathing rate assessed using measures of fractal dimension, heart rate asymmetry and permutation entropy”, Entropy, 25(2), 301 (2023). https://doi.org/10.3390/e25020301
79.Garehdaghi, F. and Sarbaz, Y. “Analyzing global features of magnetic resonance images in widespread neurodegenerative diseases: new hope to understand brain mechanism and robust neurodegenerative disease diagnosis”, Med. Biol. Eng. Comput., 61, pp. 773-784 (2023).  https://doi.org/10.1007/s11517-022-02748-0
80.Chandrasekharan, S., Jacob, J.E., Cherian, A., et al. “Exploring recurrence quantification analysis and fractal dimension algorithms for diagnosis of encephalopathy”, Cogn. Neurodyn., 18(1), pp. 1-14 (2023).  https://doi.org/10.1007/s11571-023-09929-z