Journal ArticleDOI
Nonlinear analysis of brain activity, associated with motor action and motor imaginary in untrained subjects
Vladimir A. Maksimenko,Alexey N. Pavlov,Anastasia E. Runnova,Vladimir Nedaivozov,Vadim V. Grubov,Alexey Koronovslii,Svetlana V. Pchelintseva,Elena Pitsik,Alexander N. Pisarchik,Alexander N. Pisarchik,Alexander E. Hramov,Alexander E. Hramov +11 more
TLDR
This work analyzes the spatio-temporal and time–frequency characteristics of the electrical brain activity, associated with both the motor execution and imagery in a group of untrained subjects, by applying different methods of nonlinear dynamics and creates an algorithm allowing online detection of the observed patterns.Abstract:
Identification of brain activity associated with motor execution and, more importantly, with motor imagery is necessary for the development of brain–computer interfaces. Most of recent studies were performed with trained participants which demonstrated that the motor-related brain activity can be detected from the analysis of multichannel electroencephalograms (EEG). For untrained subjects, this task is less studied, but at the same time much more challenging. This task can be solved using the methods of nonlinear dynamics, allowing to extract specific features of the neuronal network of the brain (e.g., the degree of complexity of EEG signals and degree of interaction between different brain areas). In this work, we analyze the spatio-temporal and time–frequency characteristics of the electrical brain activity, associated with both the motor execution and imagery in a group of untrained subjects, by applying different methods of nonlinear dynamics. At the first stage, we apply multifractal formalism to the analysis of EEG signals to reveal the brain areas which demonstrate the most significant distinctions between real motor actions and imaginary movement. Then, using time–frequency wavelet-based analysis of the EEG activity, we analyze in detail the structure of considered brain areas. As a result, we distinguish characteristic oscillatory patterns which occur in different areas of brain and interact with each other when the motor execution (or imagination) takes place. Finally, we create an algorithm allowing online detection of the observed patterns and experimentally verify its efficiency.read more
Citations
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Journal ArticleDOI
Artificial Neural Network Classification of Motor-Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity
Vladimir A. Maksimenko,Semen A. Kurkin,Elena Pitsik,Vyacheslav Yu. Musatov,Anastasia E. Runnova,Tatyana Yu. Efremova,Alexander E. Hramov,Alexander N. Pisarchik,Alexander N. Pisarchik +8 more
TL;DR: It is demonstrated that the filtration of high-frequency spectral components significantly enhances the classification performance and the obtained results are of particular interest for the development of brain-computer interfaces for untrained subjects.
Journal ArticleDOI
Visual and kinesthetic modes affect motor imagery classification in untrained subjects.
Parth Chholak,Guiomar Niso,Vladimir A. Maksimenko,Semen A. Kurkin,Nikita S. Frolov,Elena Pitsik,Alexander E. Hramov,Alexander N. Pisarchik +7 more
TL;DR: It is shown that it is also possible to identify particular features of MI in untrained subjects, and the application of artificial neural networks allows us to classify MI in raising right and left arms with average accuracy of 70% for both KI and VI using appropriate filtration of input signals.
Journal ArticleDOI
Physical principles of brain–computer interfaces and their applications for rehabilitation, robotics and control of human brain states
TL;DR: The physical principles of BCIs, and underlying novel approaches for registration, analysis, and control of brain activity are reviewed, and the new emerging technological trend in the BCI development which consists in using neurointerfaces to improve the interaction between people, so-called brain-to-brain interfaces (BBIs).
Journal ArticleDOI
Neural Interactions in a Spatially-Distributed Cortical Network During Perceptual Decision-Making.
Vladimir A. Maksimenko,Nikita S. Frolov,Alexander E. Hramov,Anastasia E. Runnova,Vadim V. Grubov,Jürgen Kurths,Alexander N. Pisarchik +6 more
TL;DR: It is demonstrated that RT fluctuates even when the stimulus ambiguity remains unchanged, and hypothesize that RT is affected by the processes preceding the decision-making stage, e.g., encoding visual sensory information and extracting decision-relevant features from raw sensory information.
Journal ArticleDOI
Age-related slowing down in the motor initiation in elderly adults.
Nikita S. Frolov,Elena Pitsik,Vladimir A. Maksimenko,Vadim V. Grubov,Anton R. Kiselev,Zhen Wang,Alexander E. Hramov +6 more
TL;DR: It is demonstrated that the age-related slowing down in the motor initiation before the dominant hand movements is accompanied by the increased theta activation within sensorimotor area and reconfiguration of the theta-band functional connectivity in elderly adults.
References
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