The development of new approaches to detect motor-related brain activity is key in many aspects of science, especially in brain–computer interface applications. Even though some well-known features of motor-related electroencephalograms have been revealed using traditionally applied methods, they still lack a robust classification of motor-related patterns. Here, we introduce new features of motor-related brain activity and uncover hidden mechanisms of the underlying neuronal dynamics by considering event-related desynchronization (ERD) of -rhythm in the sensorimotor cortex, i.e., tracking the decrease of the power spectral density in the corresponding frequency band. We hypothesize that motor-related ERD is associated with the suppression of random fluctuations of -band neuronal activity. This is due to the lowering of the number of active neuronal populations involved in the corresponding oscillation mode. In this case, we expect more regular dynamics and a decrease in complexity of the EEG signal recorded over the sensorimotor cortex. In order to support this, we apply measures of signal complexity by means of recurrence quantification analysis (RQA). In particular, we demonstrate that certain RQA quantifiers are very useful to detect the moment of movement onset and, therefore, are able to classify the laterality of executed movements.
REFERENCES
1.
M. E.
Stoykov
and S.
Madhavan
, “Motor priming in neurorehabilitation
,” J. Neurologic Phys. Ther.
39
, 33
(2015
). 2.
D. J.
McFarland
and J. R.
Wolpaw
, “Brain-computer interface operation of robotic and prosthetic devices
,” Computer
41
, 52
–56
(2008
). 3.
J. R.
Wolpaw
, N.
Birbaumer
, D. J.
McFarland
, G.
Pfurtscheller
, and T. M.
Vaughan
, “Brain–computer interfaces for communication and control
,” Clin. Neurophysiol.
113
, 767
–791
(2002
). 4.
U.
Chaudhary
, N.
Birbaumer
, and A.
Ramos-Murguialday
, “Brain–computer interfaces for communication and rehabilitation
,” Nat. Rev. Neurol.
12
, 513
(2016
). 5.
M. A.
Lebedev
, and M. A.
Nicolelis
, “Brain-machine interfaces: From basic science to neuroprostheses and neurorehabilitation
,” Physiol. Rev.
97
, 767
–837
(2017
). 6.
M. A.
Lebedev
and M. A.
Nicolelis
, “Brain–machine interfaces: Past, present and future
,” Trends Neurosci.
29
, 536
–546
(2006
). 7.
A. L.
Benabid
, T.
Costecalde
, A.
Eliseyev
, G.
Charvet
, A.
Verney
, S.
Karakas
, M.
Foerster
, A.
Lambert
, B.
Morinière
, N.
Abroug
et al., “An exoskeleton controlled by an epidural wireless brain–machine interface in a tetraplegic patient: A proof-of-concept demonstration
,” Lancet Neurol.
18
, 1112
–1122
(2019
).8.
D.
McFarland
and J.
Wolpaw
, “EEG-based brain–computer interfaces
,” Curr. Opin. Biomed. Eng.
4
, 194
–200
(2017
). 9.
H.
Yuan
and B.
He
, “Brain–computer interfaces using sensorimotor rhythms: Current state and future perspectives
,” IEEE Trans. Biomed. Eng.
61
, 1425
–1435
(2014
). 10.
H.
Ramoser
, J.
Muller-Gerking
, and G.
Pfurtscheller
, “Optimal spatial filtering of single trial EEG during imagined hand movement
,” IEEE. Trans. Rehabil. Eng.
8
, 441
–446
(2000
). 11.
C.
Brunner
, M.
Naeem
, R.
Leeb
, B.
Graimann
, and G.
Pfurtscheller
, “Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis
,” Pattern Recognit. Lett.
28
, 957
–964
(2007
). 12.
V. A.
Maksimenko
, S. A.
Kurkin
, E. N.
Pitsik
, V. Y.
Musatov
, A. E.
Runnova
, T. Y.
Efremova
, A. E.
Hramov
, and A. N.
Pisarchik
, “Artificial neural network classification of motor-related EEG: An increase in classification accuracy by reducing signal complexity
,” Complexity
2018
, 9385947
(2018
). 13.
P.
Chholak
, G.
Niso
, V. A.
Maksimenko
, S. A.
Kurkin
, N. S.
Frolov
, E. N.
Pitsik
, A. E.
Hramov
, and A. N.
Pisarchik
, “Visual and kinesthetic modes affect motor imagery classification in untrained subjects
,” Sci. Rep.
9
, 1
–12
(2019
). 14.
G.
Pfurtscheller
, C.
Brunner
, A.
Schlögl
, and F. L.
Da Silva
, “Mu rhythm (de) synchronization and EEG single-trial classification of different motor imagery tasks
,” NeuroImage
31
, 153
–159
(2006
). 15.
V. A.
Maksimenko
, A.
Pavlov
, A. E.
Runnova
, V.
Nedaivozov
, V.
Grubov
, A.
Koronovslii
, S. V.
Pchelintseva
, E.
Pitsik
, A. N.
Pisarchik
, and A. E.
Hramov
, “Nonlinear analysis of brain activity, associated with motor action and motor imaginary in untrained subjects
,” Nonlinear Dyn.
91
, 2803
–2817
(2018
). 16.
V. A.
Maksimenko
, A.
Lüttjohann
, V. V.
Makarov
, M. V.
Goremyko
, A. A.
Koronovskii
, V.
Nedaivozov
, A. E.
Runnova
, G.
van Luijtelaar
, A. E.
Hramov
, and S.
Boccaletti
, “Macroscopic and microscopic spectral properties of brain networks during local and global synchronization
,” Phys. Rev. E
96
, 012316
(2017
). 17.
T.-E.
Kam
, H.-I.
Suk
, and S.-W.
Lee
, “Non-homogeneous spatial filter optimization for electroencephalogram (EEG)-based motor imagery classification
,” Neurocomputing
108
, 58
–68
(2013
). 18.
J.
Asensio-Cubero
, J.
Gan
, and R.
Palaniappan
, “Multiresolution analysis over simple graphs for brain computer interfaces
,” J. Neural Eng.
10
, 046014
(2013
). 19.
V.
Grubov
, V. Y.
Musatov
, V.
Maksimenko
, A.
Pisarchik
, A.
Runnova
, and A.
Hramov
, “Development of intelligent system for classification of multiple human brain states corresponding to different real and imaginary movements
,” Cybern. Phys.
6
, 103
–107
(2017
).20.
G.
Pfurtscheller
and F. L.
Da Silva
, “Event-related EEG/MEG synchronization and desynchronization: Basic principles
,” Clin. Neurophysiol.
110
, 1842
–1857
(1999
). 21.
N.
Marwan
, M. C.
Romano
, M.
Thiel
, and J.
Kurths
, “Recurrence plots for the analysis of complex systems
,” Phys. Rep.
438
, 237
–329
(2007
). 22.
Recurrence Quantification Analysis: Theory and Best Practices
, Springer Series: Understanding Complex Systems, edited by
C. L.
Webber
and N.
Marwan
, Jr. (Springer International Publishing, Cham, Switzerland, 2015
).23.
N.
Marwan
, N.
Wessel
, U.
Meyerfeldt
, A.
Schirdewan
, and J.
Kurths
, “Recurrence-plot-based measures of complexity and their application to heart-rate-variability data
,” Phys. Rev. E
66
, 026702
(2002
). 24.
U. R.
Acharya
, E. C.-P.
Chua
, O.
Faust
, T.-C.
Lim
, and L. F. B.
Lim
, “Automated detection of sleep apnea from electrocardiogram signals using nonlinear parameters
,” Physiol. Meas.
32
, 287
(2011
). 25.
S.
Ikegawa
, M.
Shinohara
, T.
Fukunaga
, J. P.
Zbilut
, and C. L.
Webber, Jr.
“Nonlinear time-course of lumbar muscle fatigue using recurrence quantifications
,” Biol. Cybern.
82
, 373
–382
(2000
). 26.
C.
Bauer
, F.
Rast
, M.
Ernst
, A.
Meichtry
, J.
Kool
, S.
Rissanen
, J.
Suni
, and M.
Kankaanpää
, “The effect of muscle fatigue and low back pain on lumbar movement variability and complexity
,” J. Electromyogr. Kinesiol.
33
, 94
–102
(2017
). 27.
U. R.
Acharya
, S.
Bhat
, O.
Faust
, H.
Adeli
, E. C.-P.
Chua
, W. J. E.
Lim
, and J. E. W.
Koh
, “Nonlinear dynamics measures for automated EEG-based sleep stage detection
,” Eur. Neurol.
74
, 268
–287
(2015
). 28.
G. Z.
dos Santos Lima
, S. R.
Lopes
, T. L.
Prado
, B.
Lobao-Soares
, G. C.
do Nascimento
, J.
Fontenele-Araujo
, and G.
Corso
, “Predictability of arousal in mouse slow wave sleep by accelerometer data
,” PLoS ONE
12
, e0176761
(2017
). 29.
V.
Parro
and L.
Valdo
, “Sleep-wake detection using recurrence quantification analysis
,” Chaos
28
, 085706
(2018
). 30.
E. J.
Ngamga
, S.
Bialonski
, N.
Marwan
, J.
Kurths
, C.
Geier
, and K.
Lehnertz
, “Evaluation of selected recurrence measures in discriminating pre-ictal and inter-ictal periods from epileptic EEG data
,” Phys. Lett. A
380
, 1419
–1425
(2016
). 31.
T.
Rings
, M.
Mazarei
, A.
Akhshi
, C.
Geier
, M. R. R.
Tabar
, and K.
Lehnertz
, “Traceability and dynamical resistance of precursor of extreme events
,” Sci. Rep.
9
, 1744
(2019
). 32.
N.
Marwan
and A.
Meinke
, “Extended recurrence plot analysis and its application to ERP data
,” Int. J. Bifurcation Chaos
14
, 761
–771
(2004
). 33.
A. E.
Hramov
, V. A.
Maksimenko
, S. V.
Pchelintseva
, A. E.
Runnova
, V. V.
Grubov
, V. Y.
Musatov
, M. O.
Zhuravlev
, A. A.
Koronovskii
, and A. N.
Pisarchik
, “Classifying the perceptual interpretations of a bistable image using EEG and artificial neural networks
,” Front. Neurosci.
11
, 674
(2017
). 34.
C.
Letellier
, J.
Maquet
, L. L.
Sceller
, G.
Gouesbet
, and L. A.
Aguirre
, “On the non-equivalence of observables in phase-space reconstructions from recorded time series
,” J. Phys. A Math. Gen.
31
, 7913
–7927
(1998
). 35.
C.
Letellier
, I. M.
Moroz
, and R.
Gilmore
, “Comparison of tests for embeddings
,” Phys. Rev. E
78
, 026203
(2008
). 36.
C. J.
Cellucci
, A. M.
Albano
, and P. E.
Rapp
, “Comparative study of embedding methods
,” Phys. Rev. E
67
, 066210
(2003
). 37.
K.
Judd
and A.
Mees
, “Embedding as a modeling problem
,” Phys. D Nonlinear Phenom.
120
, 273
–286
(1998
). 38.
N.
Marwan
, “A historical review of recurrence plots
,” Eur. Phys. J. Spec. Top.
164
, 3
–12
(2008
). 39.
K. H.
Kraemer
, R. V.
Donner
, J.
Heitzig
, and N.
Marwan
, “Recurrence threshold selection for obtaining robust recurrence characteristics in different embedding dimensions
,” Chaos
28
, 085720
(2018
). 40.
M. S.
Baptista
, E. J.
Ngamga
, P. R. F.
Pinto
, M.
Brito
, and J.
Kurths
, “Kolmogorov-Sinai entropy from recurrence times
,” Phys. Lett. A
374
, 1135
–1140
(2010
). 41.
G.
Datseris
, “Dynamicalsystems.jl: A julia software library for chaos and nonlinear dynamics
,” J. Open Source Softw.
3
, 598
(2018
). 42.
E.
Maris
and R.
Oostenveld
, “Nonparametric statistical testing of EEG-and MEG-data
,” J. Neurosci. Methods
164
, 177
–190
(2007
). 43.
J.-M.
Schoffelen
and J.
Gross
, “Source connectivity analysis with MEG and EEG
,” Hum. Brain Mapp.
30
, 1857
–1865
(2009
). 44.
T.
Ma
, H.
Li
, L.
Deng
, H.
Yang
, X.
Lv
, P.
Li
, F.
Li
, R.
Zhang
, T.
Liu
, D.
Yao
et al., “The hybrid BCI system for movement control by combining motor imagery and moving onset visual evoked potential
,” J. Neural Eng.
14
, 026015
(2017
). 45.
A. A.
Frolov
, O.
Mokienko
, R.
Lyukmanov
, E.
Biryukova
, S.
Kotov
, L.
Turbina
, G.
Nadareyshvily
, and Y.
Bushkova
, “Post-stroke rehabilitation training with a motor-imagery-based brain-computer interface (BCI)-controlled hand exoskeleton: A randomized controlled multicenter trial
,” Front. Neurosci.
11
, 400
(2017
). 46.
A. R.
Kiselev
, V. A.
Maksimenko
, N.
Shukovskiy
, A. N.
Pisarchik
, E.
Pitsik
, and A. E.
Hramov
, “Post-stroke rehabilitation with the help of brain-computer interface,” in 2019 3rd School on Dynamics of Complex Networks and Their Application in Intellectual Robotics (DCNAIR) (IEEE, 2019), pp. 83–85.© 2020 Author(s).
2020
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