[1]

Sánchez-Granero M.A., Fernández-Martínez M., Trinidad Segovia J.E., Introducing fractal dimension algorithms to calculate the Hurst exponent of financial time series, Eur. Phys. J. B, 2012, 85: 86, https://doi.org/10.1140/epjb/e2012-20803-2

[2]

Rodríguez-Bermúdez G., García-Laencina P.J., Analysis of EEG Signals using Nonlinear Dynamics and Chaos: A review, Appl. Math. Inf. Sci., 2015, 9, 2309–2321. Google Scholar

[3]

Palaniappan R., Biological Signal Analysis, Ventus Publishing ApS, 2010. Google Scholar

[4]

Schomer D. L., Da Silva F. L., Niedermeyer’s Electroencephalography, Basic Principles, Clinical Applications, and Related Fields, 6th ed., Lippincott Williams & Wilkins, 2011. Google Scholar

[5]

Mandelbrot B.B., Wallis J.R., Robustness of the rescaled range R/S in the measurement of noncyclic long run statistical dependence, Water Resour. Res., 1969, 5, 967–988. Google Scholar

[6]

Hurst H.E., Long-term storage capacity of reservoirs, Transactions of the American Society of Civil Engineers, 1951, 116, 770–799. Google Scholar

[7]

Mandelbrot B.B., When Can Price be Arbitraged Efflciently? A Limit to the Validity of the Random Walk and Martingale Models, Rev. Econ. Stat., 1971, 53, 225–236. Google Scholar

[8]

Mandelbrot B.B., Statistical Methodology for Nonperiodic Cycles: From the Covariance to R/S Analysis, Annals of Economic and Social Measurement, 1972, 1, 259–290. Google Scholar

[9]

Mandelbrot B.B., Fractals and Scaling in Finance. Discontinuity, Concentration, Risk. Selecta Volume E, 1st ed., Springer-Verlag, New York, 1997. Google Scholar

[10]

Peng C.-K., Buldyrev S.V., Havlin S., Simons M., Stanley H.E., Goldberger A.L., Mosaic organization of DNA nucleotides, Phys. Rev. E, 1994, 49, 1685–1689. Google Scholar

[11]

Ausloos M., Statistical physics in foreign exchange currency and stock markets, Physica A, 2000, 285, 48–65. Google Scholar

[12]

Di Matteo T., Aste T., Dacorogna M.M., Long-term memories of developed and emerging markets: Using the scaling analysis to characterize their stage of development, J. Bank Financ., 2005, 29, 827–851. Google Scholar

[13]

Liu Y., Cizeau P., Meyer M., Peng C.-K., Stanley H.E., Correlations in economic time series, Physica A, 1997, 245, 437–440. Google Scholar

[14]

Montanari A., Taqqu M.S., Teverovsky V., Estimating long-range dependence in the presence of periodicity: An empirical study, Math. Comput. Model., 1999, 29, 217–228. Google Scholar

[15]

Sánchez Granero M.A., Trinidad Segovia J.E., García Pérez J., Some comments on Hurst exponent and the long memory processes on capital markets, Physica A, 2008, 387, 5543–5551. Google Scholar

[16]

Trinidad Segovia J.E., Fernández-Martínez M., Sánchez-Granero M.A., A note on geometric method-based procedures to calculate the Hurst exponent, Physica A, 2012, 391, 2209–2214. Google Scholar

[17]

Stam C.J., Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field, Clin. Neurophysiol., 2005, 116, 2266–2301. Google Scholar

[18]

Wang Q., Sourina O., Nguyen M.K., Fractal dimension based neurofeedback in serious games, Visual Comput., 2011, 27, 299–309. Google Scholar

[19]

Lopes R., Betrouni N., Fractal and multifractal analysis: A review, Med. Image Anal., 2009, 13, 634–649. Google Scholar

[20]

Iasemidis L.D., Sackellares J.C., The evolution with time of the spatial distribution of the largest Lyapunov exponent on the human epileptic cortex, In: Duke, D.W. and Pritchard, W.S. (Eds.), Measuring Chaos in the human brain, World Scientific, Singapore, 1991. Google Scholar

[21]

Mormann F., Andrzejak R.G., Elger C.E., Lehnertz K., Seizure prediction: the long and winding road, Brain, 2007, 130, 314–333. Google Scholar

[22]

Dang Khoa T.Q., Minh Huong N.T., Van Toi, V., Detecting Epileptic Seizure from Scalp EEG Using Lyapunov Spectrum, Comput.Math. Method Med., 2012, http://dx.doi.org/10.1155/2012/847686

[23]

Fergus P., Hignett D., Hussain A., Al-Jumeily D., Abdel-Aziz K., Automatic Epileptic Seizure Detection Using Scalp EEG and Advanced Artificial Intelligence Techniques, Biomed Res. Int., 2015, http://dx.doi.org/10.1155/2015/986736

[24]

Yuan Q., Zhou W., Li S., Cai D., Epileptic EEG classification based on extreme learning machine and nonlinear features, Epilepsy Res., 2011, 96, 29–38. Google Scholar

[25]

Acharya U.R., Sree S.V., Chuan Alvin A.P., Yanti R., Suri J.S., Application of non–linearand wavelet based features for the automated identification of epileptic EEG signals, Int. J. Neural Syst., 2012, 22, https://doi.org/10.1142/S0129065712500025 Google Scholar

[26]

Zhou W., Liu Y., Yuan Q., Li X., Epileptic Seizure Detection Using Lacunarity and Bayesian Linear Discriminant Analysis in Intracranial EEG, IEEE Trans. Biomed. Eng., 2013, 60, 3375–3381. Google Scholar

[27]

Namazi H., Kulish V.V., Hussaini J., Hussaini J., Delaviz A., Delaviz F., Habibi S., Ramezanpoor S., A signal processing based analysis and prediction of seizure onset in patients with epilepsy, Oncotarget, 2016, 7, 342–350. Google Scholar

[28]

Upadhyay R., Padhy P.K., Kankar P.K., A comparative study of feature ranking techniques for epileptic seizure detection using wavelet transform, Comput. Electr. Eng., 2016, 53, 163–176. Google Scholar

[29]

Cusenza M., Fractal analysis of the EEG and clinical applications, PhD thesis, Università degli studi di Trieste, Italy, 2012. Google Scholar

[30]

Shoushtarian M., Sahinovic M.M., Absalom A.R., Kalmar A.F., Vereecke H.E.M., Liley D.T.J., Struys M.R.F., Comparisons of Electroencephalographically Derived Measures of Hypnosis and Antinociception in Response to Standardized Stimuli During Target-Controlled Propofol-Remifentanil Anesthesia, Anesth. Analg., 2016, 122, 382–392. Google Scholar

[31]

Shalbaf R., Behnam H., Moghadam H.J., Monitoring depth of anesthesia using combination of EEG measure and hemodynamic variables, Cogn. Neurodynamics, 2015, 9, 41–51. Google Scholar

[32]

Shalbaf R., Behnam H., Sleigh J.W., Steyn-Ross A., Voss L.J., Monitoring the depth of anesthesia using entropy features and an artificial neural network, J. Neurosci. Methods, 2013, 218, 17–24. Google Scholar

[33]

Pradhan C., Jena S.K., Nadar S.R., Pradhan N., Higher-Order Spectrum in Understanding Nonlinearity in EEG Rhythms, Comput. Math. Method Med., 2012, http://dx.doi.org/10.1155/2012/206857

[34]

Zoughi T., Boostani R., Deypir M., A wavelet-based estimating depth of anesthesia, Eng. Appl. Artif. Intell., 2012, 25, 1710 1722. Google Scholar

[35]

Kuhlmann L., Freestone D.R., Manton J.H., Heyse B., Vereecke H.E.M., Lipping T., Struys M.M.R.F., Liley D.T.J., Neural mass model-based tracking of anesthetic brain states, NeuroImage, 2016, 133, 438–456. Google Scholar

[36]

Kuhlmann L.,Manton J.H., Heyse B., Vereecke H.E.M., Lipping T., Struys M.M.R.F., Liley D.T.J., Tracking Electroencephalographic Changes Using Distributions of Linear Models: Application to Propofol-Based Depth of Anesthesia Monitoring, IEEE Trans. Biomed. Eng., 2017, 64, 870–881. Google Scholar

[37]

Buckley A.W., Scott R., Tyler A., Matthew Mahoney J., Thurm A., Farmer C., Swedo S., Burroughs S.A., Holmes G.L., State-Dependent Differences in Functional Connectivity in Young Children With Autism Spectrum Disorder, EBioMedicine, 2015, 2, 1905–1915. Google Scholar

[38]

Ahmadlou M., Adeli H., Adeli A., Fractality and a Wavelet-Chaos-Neural Network Methodology for EEG-Based Diagnosis of Autistic Spectrum Disorder, J. Clin. Neurophysiol., 2010, 27, 328–333. Google Scholar

[39]

Akar S.A., Kara S., Agambayev S., Bilgiç V., Nonlinear analysis of EEG in major depression with fractal dimensions, In: Engineering in Medicine and Biology Society (EMBC), 37th Annual International Conference of the IEEE, 2015, 7410–7413. Google Scholar

[40]

Hosseinifard B., Hassan Moradi M., Rostami R., Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal, Comput. Meth. Programs Biomed., 2013, 109, 339–345. Google Scholar

[41]

Bachmann M., Lass J., Suhhova A., Hinrikus H., Spectral Asymmetry and Higuchi’s Fractal Dimension Measures of Depression Electroencephalogram, Comput. Math. Method Med., 2013, http://dx.doi.org/10.1155/2013/251638

[42]

Ahmadlou M., Adeli H., Adeli A., Fractality analysis of frontal brain in major depressive disorder, Int. J. Psychophysiol., 2012, 85, 206–211. Google Scholar

[43]

Acharya U.R., Sudarshan V.K., Adeli H., Santhosh J., Koh J.E.W., Puthankatti S.D., Adeli A., A Novel Depression Diagnosis Index Using Nonlinear Features in EEG Signals, Eur. Neurol., 2015, 74, 79–83. Google Scholar

[44]

Mizuno T., Takahashi T., Cho R.Y., Kikuchi M., Murata T., Takahashi K., Wada Y., Assessment of EEG dynamical complexity in Alzheimer’s disease using multiscale entropy, Clin. Neurophysiol., 2010, 121, 1438–1446. Google Scholar

[45]

Ahmadlou M., Adeli H., Adeli A., Fractality and a Wavelet-chaos-Methodology for EEG-based Diagnosis of Alzheimer Disease, Alzheimer Dis. Assoc. Dis., 2011, 25, 85–92. Google Scholar

[46]

Smits F.M., Porcaro C., Cottone C., Cancelli A., Rossini P.M., Tecchio F., Electroencephalographic Fractal Dimension in Healthy Ageing and Alzheimer’s Disease, PLoS One, 2016, https://doi.org/10.1371/journal.pone.0149587

[47]

Lainscsek C., Hernandez M.E., Weyhenmeyer J., Sejnowski T.J., Poizner H., non–lineardynamical analysis of EEG time series distinguishes patients with Parkinson’s disease from healthy individuals, Front. Neurol., 2013, https://doi.org/10.3389/fneur.2013.00200

[48]

Yuvaraj R.,Murugappan M., Hemispheric asymmetry non–linearanalysis of EEG during emotional responses from idiopathic Parkinson’s disease patients, Cogn. Neurodynamics, 2016, 10, 225–234. Google Scholar

[49]

Sabeti M., Katebi S.D., Boostani R., Price G.W., A new approach for EEG signal classification of schizophrenic and control participants, Expert Syst. Appl., 2011, 38, 2063–2071. Google Scholar

[50]

Yu Y., Zhao Y., Si Y., Ren Q., Ren W., Jing C., Zhang H., Estimation of the cool executive function using frontal electroencephalogram signals in first-episode schizophrenia patients, Biomed. Eng. Online, 2016, 15: 131, https://doi.org/10.1186/s12938-016-0282-y

[51]

Wolpaw J.R., Birbaumer N., McFarland D.J., Pfurtscheller G., Vaughan T.M., Brain-computer interfaces for communication and control, Clin. Neurophysiol., 2002, 113, 767–791. Google Scholar

[52]

Nicolas-Alonso L.F., Gomez-Gil J., Brain Computer Interfaces, a Review, Sensors, 2012, 12, 1211–1279. Google Scholar

[53]

Lotte F., Congedo M., Lécuyer A., Lamarche F., Arnaldi B., A review of classification algorithms for EEG-based brain-computer interfaces, J. Neural Eng., 2007, 4, R1-R13. Google Scholar

[54]

Hsu W.-Y., EEG-based motor imagery classification using neuro-fuzzy prediction and wavelet fractal features, J. Neurosci. Methods, 2010, 189, 295–302. Google Scholar

[55]

HsuW.-Y., Continuous EEG signal analysis for asynchronous BCI application, Int. J. Neural Syst., 2011, 21, 335–450. Google Scholar

[56]

Hsu W.-Y., Single-trial motor imagery classification using asymmetry ratio, phase relation, wavelet-based fractal, and their selected combination, Int. J. Neural Syst., 2013, 23, https://doi.org/10.1142/S012906571350007X Google Scholar

[57]

Esfahani E.T., Sundararajan V., Using brain-computer interfaces to detect human satisfaction in human-robot interaction, Int. J. Humanoid Robot., 2011, 8, 87–101. Google Scholar

[58]

Loo C.K., Samraj A., Lee G.C., Evaluation of Methods for Estimating Fractal Dimension in Motor Imagery-Based Brain Computer Interface, Discrete Dyn. Nat. Soc., 2011, http://dx.doi.org/10.1155/2011/724697

[59]

Wang Q., Sourina O., Real-Time Mental Arithmetic Task Recognition From EEG Signals, IEEE Trans. Neural Syst. Rehabil. Eng., 2013, 21, 225–232. Google Scholar

[60]

Rodríguez-Bermúdez G., Sánchez-Granero M.A., García-Laencina P.J., Fernández-Martínez M., Serna J., Roca-Dorda J., Testing the Self-Similarity Exponent to Feature Extraction in Motor Imagery Based Brain Computer Interface Systems, Int. J. Bifurcation Chaos, 2015, 25, https://doi.org/10.1142/S0218127415400234

[61]

Calvo R.A., D’Mello S., Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications, IEEE Trans. Affect. Comput., 2010, 1, 18–37. Google Scholar

[62]

Sourina O., Liu Y., Nguyen M.K., Real-time EEG-based emotion recognition for music therapy, J. Multimodal User Interfaces, 2012, 5, 27–35. Google Scholar

## Comments (0)

General note:By using the comment function on degruyter.com you agree to our Privacy Statement. A respectful treatment of one another is important to us. Therefore we would like to draw your attention to our House Rules.