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 …
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 …
Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users
Motor Imagery Brain-Computer Interfaces (MI-BCIs) are AI-driven systems that capture brain
activity patterns associated with mental imagination of movement and convert them into …
activity patterns associated with mental imagination of movement and convert them into …
Ensemble machine learning-based affective computing for emotion recognition using dual-decomposed EEG signals
Machine learning (ML)-based algorithms have shown promising results in
electroencephalogram (EEG)-based emotion recognition. This study compares five …
electroencephalogram (EEG)-based emotion recognition. This study compares five …
Autism spectrum disorder diagnostic system using HOS bispectrum with EEG signals
Autistic individuals often have difficulties expressing or controlling emotions and have poor
eye contact, among other symptoms. The prevalence of autism is increasing globally, posing …
eye contact, among other symptoms. The prevalence of autism is increasing globally, posing …
Classifying the perceptual interpretations of a bistable image using EEG and artificial neural networks
In order to classify different human brain states related to visual perception of ambiguous
images, we use an artificial neural network (ANN) to analyze multichannel EEG. The …
images, we use an artificial neural network (ANN) to analyze multichannel EEG. The …
Pass: a multimodal database of physical activity and stress for mobile passive body/brain-computer interface research
With the burgeoning of wearable devices and passive body/brain-computer interfaces
(B/BCIs), automated stress monitoring in everyday settings has gained significant attention …
(B/BCIs), automated stress monitoring in everyday settings has gained significant attention …
Feature extraction of EEG signal by power spectral density for motor imagery based BCI
Signals produced from the brain are widely known as Electroencephalogram (EEG) signal
interfacing with any communication device creates a unidirectional communicating channel …
interfacing with any communication device creates a unidirectional communicating channel …
Deep convolutional neural network based eye states classification using ear-EEG
Electroencephalography measured around the ear (ear-EEG) has been considered as an
effective measurement for the development of practical EEG-based applications because it …
effective measurement for the development of practical EEG-based applications because it …
Automated detection of Alzheimer's disease using bi-directional empirical model decomposition
The build-up of beta-amyloid and rapid spread of tau proteins in the brain cause the death of
neurons, leading to Alzheimer's disease (AD). AD is a form of dementia, and the symptoms …
neurons, leading to Alzheimer's disease (AD). AD is a form of dementia, and the symptoms …