A review of critical challenges in MI-BCI: From conventional to deep learning methods
Brain-computer interfaces (BCIs) have achieved significant success in controlling external
devices through the Electroencephalogram (EEG) signal processing. BCI-based Motor …
devices through the Electroencephalogram (EEG) signal processing. BCI-based Motor …
Subject inefficiency phenomenon of motor imagery brain-computer interface: Influence factors and potential solutions
Motor imagery brain–computer interfaces (MI‐BCIs) have great potential value in prosthetics
control, neurorehabilitation, and gaming; however, currently, most such systems only …
control, neurorehabilitation, and gaming; however, currently, most such systems only …
Differentiation of schizophrenia by combining the spatial EEG brain network patterns of rest and task P300
The P300 is regarded as a psychosis endophenotype of schizophrenia and a putative
biomarker of risk for schizophrenia. However, the brain activity (ie, P300 amplitude) during …
biomarker of risk for schizophrenia. However, the brain activity (ie, P300 amplitude) during …
A hybrid-domain deep learning-based BCI for discriminating hand motion planning from EEG sources
In this paper, a hybrid-domain deep learning (DL)-based neural system is proposed to
decode hand movement preparation phases from electroencephalographic (EEG) …
decode hand movement preparation phases from electroencephalographic (EEG) …
Granger causal inference based on dual laplacian distribution and its application to MI-BCI classification
Granger causality-based effective brain connectivity provides a powerful tool to probe the
neural mechanism for information processing and the potential features for brain computer …
neural mechanism for information processing and the potential features for brain computer …
A survey of brain network analysis by electroencephalographic signals
Brain network analysis is one efficient tool in exploring human brain diseases and can
differentiate the alterations from comparative networks. The alterations account for time …
differentiate the alterations from comparative networks. The alterations account for time …
A new dispersion entropy and fuzzy logic system methodology for automated classification of dementia stages using electroencephalograms
JP Amezquita-Sanchez, N Mammone… - Clinical Neurology and …, 2021 - Elsevier
A new EEG-based methodology is presented for differential diagnosis of the Alzheimer's
disease (AD), Mild Cognitive Impairment (MCI), and healthy subjects employing the discrete …
disease (AD), Mild Cognitive Impairment (MCI), and healthy subjects employing the discrete …
Functional connectivity analysis in motor-imagery brain computer interfaces
N Leeuwis, S Yoon, M Alimardani - Frontiers in Human Neuroscience, 2021 - frontiersin.org
Motor Imagery BCI systems have a high rate of users that are not capable of modulating their
brain activity accurately enough to communicate with the system. Several studies have …
brain activity accurately enough to communicate with the system. Several studies have …
A novel method for constructing EEG large-scale cortical dynamical functional network connectivity (dFNC): WTCS
As a kind of biological network, the brain network conduces to understanding the mystery of
high-efficiency information processing in the brain, which will provide instructions to develop …
high-efficiency information processing in the brain, which will provide instructions to develop …
Long-term kinesthetic motor imagery practice with a BCI: Impacts on user experience, motor cortex oscillations and BCI performances
Kinesthetic motor imagery (KMI) generates specific brain patterns in sensorimotor rhythm
over the motor cortex (called event-related (de)-synchronization, ERD/ERS), allowing KMI to …
over the motor cortex (called event-related (de)-synchronization, ERD/ERS), allowing KMI to …