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.
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Motor execution reduces EEG signals complexity: Recurrence quantification analysis study
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February 2020
Research Article|
February 04 2020
Motor execution reduces EEG signals complexity: Recurrence quantification analysis study
Elena Pitsik
;
Elena Pitsik
1
Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University
, 420500 Innopolis, The Republic of Tatarstan, Russia
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Nikita Frolov
;
Nikita Frolov
a)
1
Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University
, 420500 Innopolis, The Republic of Tatarstan, Russia
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K. Hauke Kraemer;
K. Hauke Kraemer
2
Potsdam Institute for Climate Impact Research
, 14473 Potsdam, Germany
3
Institute of Geosciences, University of Potsdam
, 14476 Potsdam-Golm, Germany
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Vadim Grubov
;
Vadim Grubov
1
Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University
, 420500 Innopolis, The Republic of Tatarstan, Russia
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Vladimir Maksimenko
;
Vladimir Maksimenko
1
Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University
, 420500 Innopolis, The Republic of Tatarstan, Russia
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Jürgen Kurths
;
Jürgen Kurths
2
Potsdam Institute for Climate Impact Research
, 14473 Potsdam, Germany
4
Department of Physics, Humboldt University
, 12489 Berlin, Germany
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Alexander Hramov
Alexander Hramov
a)
1
Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University
, 420500 Innopolis, The Republic of Tatarstan, Russia
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a)
Authors to whom correspondence should be addressed: n.frolov@innopolis.ru and a.hramov@innopolis.ru
Chaos 30, 023111 (2020)
Article history
Received:
November 12 2019
Accepted:
December 27 2019
Citation
Elena Pitsik, Nikita Frolov, K. Hauke Kraemer, Vadim Grubov, Vladimir Maksimenko, Jürgen Kurths, Alexander Hramov; Motor execution reduces EEG signals complexity: Recurrence quantification analysis study. Chaos 1 February 2020; 30 (2): 023111. https://doi.org/10.1063/1.5136246
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