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 …
Review and classification of emotion recognition based on EEG brain-computer interface system research: a systematic review
Recent developments and studies in brain-computer interface (BCI) technologies have
facilitated emotion detection and classification. Many BCI studies have sought to investigate …
facilitated emotion detection and classification. Many BCI studies have sought to investigate …
EEG-based emotion recognition using regularized graph neural networks
Electroencephalography (EEG) measures the neuronal activities in different brain regions
via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit …
via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit …
EEG-based emotion recognition using an end-to-end regional-asymmetric convolutional neural network
Emotion recognition based on electroencephalography (EEG) is of great important in the
field of Human–Computer Interaction (HCI), which has received extensive attention in recent …
field of Human–Computer Interaction (HCI), which has received extensive attention in recent …
Automated EEG-based screening of depression using deep convolutional neural network
In recent years, advanced neurocomputing and machine learning techniques have been
used for Electroencephalogram (EEG)-based diagnosis of various neurological disorders. In …
used for Electroencephalogram (EEG)-based diagnosis of various neurological disorders. In …
DepHNN: a novel hybrid neural network for electroencephalogram (EEG)-based screening of depression
Depression is a psychological disorder characterized by the continuous occurrence of bad
mood state. It is critical to understand that this disorder is severely affecting people of …
mood state. It is critical to understand that this disorder is severely affecting people of …
Automated accurate detection of depression using twin Pascal's triangles lattice pattern with EEG Signals
Electroencephalogram (EEG)-based major depressive disorder (MDD) machine learning
detection models can objectively differentiate MDD from healthy controls but are limited by …
detection models can objectively differentiate MDD from healthy controls but are limited by …
Automated detection of schizophrenia using nonlinear signal processing methods
Examination of the brain's condition with the Electroencephalogram (EEG) can be helpful to
predict abnormality and cerebral activities. The purpose of this study was to develop an …
predict abnormality and cerebral activities. The purpose of this study was to develop an …
EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review
Mental disorders represent critical public health challenges as they are leading contributors
to the global burden of disease and intensely influence social and financial welfare of …
to the global burden of disease and intensely influence social and financial welfare of …
MS-MDA: Multisource marginal distribution adaptation for cross-subject and cross-session EEG emotion recognition
As an essential element for the diagnosis and rehabilitation of psychiatric disorders, the
electroencephalogram (EEG) based emotion recognition has achieved significant progress …
electroencephalogram (EEG) based emotion recognition has achieved significant progress …