Physical principles of brain–computer interfaces and their applications for rehabilitation, robotics and control of human brain states
Brain–computer interfaces (BCIs) development is closely related to physics. In this paper, we
review the physical principles of BCIs, and underlying novel approaches for registration …
review the physical principles of BCIs, and underlying novel approaches for registration …
Functional networks of the brain: from connectivity restoration to dynamic integration
A review of physical and mathematical methods for reconstructing the functional networks of
the brain based on recorded brain activity is presented. Various methods are considered, as …
the brain based on recorded brain activity is presented. Various methods are considered, as …
Introduction to focus issue: When machine learning meets complex systems: Networks, chaos, and nonlinear dynamics
Machine learning (ML), a subset of artificial intelligence, refers to methods that have the
ability to “learn” from experience, enabling them to carry out designated tasks. Examples of …
ability to “learn” from experience, enabling them to carry out designated tasks. Examples of …
Deep convolutional neural network-based visual stimuli classification using electroencephalography signals of healthy and alzheimer's disease subjects
Visual perception is an important part of human life. In the context of facial recognition, it
allows us to distinguish between emotions and important facial features that distinguish one …
allows us to distinguish between emotions and important facial features that distinguish one …
Stimulus classification using chimera-like states in a spiking neural network
A complex network of bistable Hodgkin-Huxley (HH) neurons with excitatory coupling can
exhibit a partially spiking chimera behavior. We propose to use this chimera-like state for …
exhibit a partially spiking chimera behavior. We propose to use this chimera-like state for …
Identification of alzheimer's eeg with a wvg network-based fuzzy learning approach
H Yu, L Zhu, L Cai, J Wang, J Liu, R Wang… - Frontiers in …, 2020 - frontiersin.org
A novel analytical framework combined fuzzy learning and complex network approaches is
proposed for the identification of Alzheimer's disease (AD) with multichannel scalp-recorded …
proposed for the identification of Alzheimer's disease (AD) with multichannel scalp-recorded …
Explainable machine learning methods for classification of brain states during visual perception
The aim of this work is to find a good mathematical model for the classification of brain states
during visual perception with a focus on the interpretability of the results. To achieve it, we …
during visual perception with a focus on the interpretability of the results. To achieve it, we …
[HTML][HTML] System for monitoring and adjusting the learning process of primary schoolchildren based on the eeg data analysis
Introduction: Monitoring the learning process usually involves an analysis of the higher
mental functions of the student: imagination, memory, thinking, attention, etc. Currently, there …
mental functions of the student: imagination, memory, thinking, attention, etc. Currently, there …
Combining statistical analysis and machine learning for eeg scalp topograms classification
Incorporating brain-computer interfaces (BCIs) into daily life requires reducing the reliance
of decoding algorithms on the calibration or enabling calibration with the minimal burden on …
of decoding algorithms on the calibration or enabling calibration with the minimal burden on …
On the characterization of cognitive tasks using activity-specific short-lived synchronization between electroencephalography channels
Accurate characterization of brain activity during a cognitive task is challenging due to the
dynamically changing and the complex nature of the brain. The majority of the proposed …
dynamically changing and the complex nature of the brain. The majority of the proposed …