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 …
Coherence resonance in neural networks: Theory and experiments
The paper is devoted to the review of the coherence resonance phenomenon in excitable
neural networks. In particular, we explain how coherence can be measured and how noise …
neural networks. In particular, we explain how coherence can be measured and how noise …
Visual and kinesthetic modes affect motor imagery classification in untrained subjects
The understanding of neurophysiological mechanisms responsible for motor imagery (MI) is
essential for the development of brain-computer interfaces (BCI) and bioprosthetics. Our …
essential for the development of brain-computer interfaces (BCI) and bioprosthetics. Our …
Epileptic seizure detection using a hybrid 1D CNN‐machine learning approach from EEG data
Electroencephalography (EEG) is a widely used technique for the detection of epileptic
seizures. It can be recorded in a noninvasive manner to present the electrical activity of the …
seizures. It can be recorded in a noninvasive manner to present the electrical activity of the …
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 …
Classification of soybean genotypes for industrial traits using UAV multispectral imagery and machine learning
Soybean genotypes have distinct physicochemical characteristics, mainly regarding the oil
and protein contents in the grains. The use of high-throughput phe-noty** technologies …
and protein contents in the grains. The use of high-throughput phe-noty** technologies …
Identification of motor and mental imagery EEG in two and multiclass subject-dependent tasks using successive decomposition index
The development of fast and robust brain–computer interface (BCI) systems requires non-
complex and efficient computational tools. The modern procedures adopted for this purpose …
complex and efficient computational tools. The modern procedures adopted for this purpose …
Recurrent neural networks with TF-IDF embedding technique for detection and classification in tweets of dengue disease
With the increased usage of Web 2.0 and data-affluent tools such as social media platforms
and web blog services, the challenge of extracting public sentiment and disseminating …
and web blog services, the challenge of extracting public sentiment and disseminating …
Motor execution reduces EEG signals complexity: Recurrence quantification analysis study
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 …
aspects of science, especially in brain–computer interface applications. Even though some …
Attention deficit hyperactivity disorder detection in children using multivariate empirical EEG decomposition approaches: A comprehensive analytical study
Early detection and timely therapeutic intervention are of prime importance to prevent the
severity of attention deficit hyperactivity disorder (ADHD) in children. Conventional …
severity of attention deficit hyperactivity disorder (ADHD) in children. Conventional …