Motor execution reduces EEG signals complexity: Recurrence quantification analysis study.
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Elena Pitsik | Nikita Frolov | Jürgen Kurths | Vladimir Maksimenko | K Hauke Kraemer | Vadim Grubov | Alexander Hramov | J. Kurths | V. Maksimenko | N. Frolov | A. Hramov | E. Pitsik | V. Grubov | K. Hauke Kraemer
[1] Luis A. Aguirre,et al. On the non-equivalence of observables in phase-space reconstructions from recorded time series , 1998 .
[2] V C Parro,et al. Sleep-wake detection using recurrence quantification analysis. , 2018, Chaos.
[3] J. Kurths,et al. Recurrence-plot-based measures of complexity and their application to heart-rate-variability data. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.
[4] Clemens Brunner,et al. Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis , 2007, Pattern Recognit. Lett..
[5] M. Nicolelis,et al. Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. , 2017, Physiological reviews.
[6] Joel E. W. Koh,et al. Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection , 2015, European Neurology.
[7] Vladimir A. Maksimenko,et al. Post-stroke rehabilitation with the help of brain-computer interface , 2019, 2019 3rd School on Dynamics of Complex Networks and their Application in Intellectual Robotics (DCNAIR).
[8] J R Wolpaw,et al. EEG-Based Brain-Computer Interfaces. , 2017, Current opinion in biomedical engineering.
[9] R. Gilmore,et al. Comparison of tests for embeddings. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.
[10] Anastasiya E. Runnova,et al. Classifying the Perceptual Interpretations of a Bistable Image Using EEG and Artificial Neural Networks , 2017, Front. Neurosci..
[11] Sangeetha Madhavan,et al. Motor Priming in Neurorehabilitation , 2015, Journal of neurologic physical therapy : JNPT.
[12] P. Rapp,et al. Comparative study of embedding methods. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.
[13] Norbert Marwan,et al. Extended Recurrence Plot Analysis and its Application to ERP Data , 2002, Int. J. Bifurc. Chaos.
[14] J. Kurths,et al. Kolmogorov–Sinai entropy from recurrence times , 2009, 0908.3401.
[15] Miguel A. L. Nicolelis,et al. Brain–machine interfaces: past, present and future , 2006, Trends in Neurosciences.
[16] R. Oostenveld,et al. Nonparametric statistical testing of EEG- and MEG-data , 2007, Journal of Neuroscience Methods.
[17] S. R. Lopes,et al. Predictability of arousal in mouse slow wave sleep by accelerometer data , 2017, PloS one.
[18] Dennis J. McFarland,et al. Brain-Computer Interface Operation of Robotic and Prosthetic Devices , 2008, Computer.
[19] George Datseris,et al. DynamicalSystems.jl: A Julia software library for chaos and nonlinear dynamics , 2018, J. Open Source Softw..
[20] N. Birbaumer,et al. Brain–computer interfaces for communication and rehabilitation , 2016, Nature Reviews Neurology.
[21] E. Biryukova,et al. Post-stroke Rehabilitation Training with a Motor-Imagery-Based Brain-Computer Interface (BCI)-Controlled Hand Exoskeleton: A Randomized Controlled Multicenter Trial , 2017, Front. Neurosci..
[22] J. Q. Gan,et al. Multiresolution analysis over simple graphs for brain computer interfaces , 2013, Journal of neural engineering.
[23] Bin He,et al. Brain–Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives , 2014, IEEE Transactions on Biomedical Engineering.
[24] Norbert Marwan,et al. A historical review of recurrence plots , 2008, 1709.09971.
[25] U. Acharya,et al. Automated detection of sleep apnea from electrocardiogram signals using nonlinear parameters , 2011, Physiological measurement.
[26] Jürgen Kurths,et al. Recurrence plots for the analysis of complex systems , 2009 .
[27] Hao Yang,et al. The hybrid BCI system for movement control by combining motor imagery and moving onset visual evoked potential , 2017, Journal of neural engineering.
[28] A. Benabid,et al. An exoskeleton controlled by an epidural wireless brain–machine interface in a tetraplegic patient: a proof-of-concept demonstration , 2019, The Lancet Neurology.
[29] Heung-Il Suk,et al. Non-homogeneous spatial filter optimization for ElectroEncephaloGram (EEG)-based motor imagery classification , 2013, Neurocomputing.
[30] Parth Chholak,et al. Visual and kinesthetic modes affect motor imagery classification in untrained subjects , 2019, Scientific Reports.
[31] Clemens Brunner,et al. Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks , 2006, NeuroImage.
[32] Dennis J. McFarland,et al. Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.
[33] Klaus Lehnertz,et al. Traceability and dynamical resistance of precursor of extreme events , 2019, Scientific Reports.
[34] S M Rissanen,et al. The effect of muscle fatigue and low back pain on lumbar movement variability and complexity. , 2017, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.
[35] Stefano Boccaletti,et al. Macroscopic and microscopic spectral properties of brain networks during local and global synchronization. , 2017, Physical review. E.
[36] Vladimir A. Maksimenko,et al. Artificial Neural Network Classification of Motor-Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity , 2018, Complex..
[37] G. Pfurtscheller,et al. Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.
[38] Norbert Marwan,et al. Recurrence threshold selection for obtaining robust recurrence characteristics in different embedding dimensions. , 2018, Chaos.
[39] Klaus Lehnertz,et al. Evaluation of selected recurrence measures in discriminating pre-ictal and inter-ictal periods from epileptic EEG data , 2016, 1602.07974.
[40] Charles L. Webber,et al. Nonlinear time-course of lumbar muscle fatigue using recurrence quantifications , 2000, Biological Cybernetics.
[41] F. L. D. Silva,et al. Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.
[42] J. Schoffelen,et al. Source connectivity analysis with MEG and EEG , 2009, Human brain mapping.
[43] Alexey N. Pavlov,et al. Nonlinear analysis of brain activity, associated with motor action and motor imaginary in untrained subjects , 2018 .
[44] Kevin Judd,et al. Embedding as a modeling problem , 1998 .