Support vector machines to detect physiological patterns for EEG and EMG-based human–computer interaction: a review
Support vector machines (SVMs) are widely used classifiers for detecting physiological
patterns in human–computer interaction (HCI). Their success is due to their versatility …
patterns in human–computer interaction (HCI). Their success is due to their versatility …
Progress in EEG‐Based Brain Robot Interaction Systems
The most popular noninvasive Brain Robot Interaction (BRI) technology uses the
electroencephalogram‐(EEG‐) based Brain Computer Interface (BCI), to serve as an …
electroencephalogram‐(EEG‐) based Brain Computer Interface (BCI), to serve as an …
A computerized method for automatic detection of schizophrenia using EEG signals
Diagnosis of schizophrenia (SZ) is traditionally performed through patient's interviews by a
skilled psychiatrist. This process is time-consuming, burdensome, subject to error and bias …
skilled psychiatrist. This process is time-consuming, burdensome, subject to error and bias …
A deep transfer convolutional neural network framework for EEG signal classification
Nowadays, motor imagery (MI) electroencephalogram (EEG) signal classification has
become a hotspot in the research field of brain computer interface (BCI). More recently, deep …
become a hotspot in the research field of brain computer interface (BCI). More recently, deep …
Convolutional neural network based approach towards motor imagery tasks EEG signals classification
This paper introduces a methodology based on deep convolutional neural networks (DCNN)
for motor imagery (MI) tasks recognition in the brain-computer interface (BCI) system. More …
for motor imagery (MI) tasks recognition in the brain-computer interface (BCI) system. More …
A new framework for automatic detection of patients with mild cognitive impairment using resting-state EEG signals
Mild cognitive impairment (MCI) can be an indicator representing the early stage of
Alzheimier's disease (AD). AD, which is the most common form of dementia, is a major …
Alzheimier's disease (AD). AD, which is the most common form of dementia, is a major …
Exploiting dimensionality reduction and neural network techniques for the development of expert brain–computer interfaces
Background: Analysis and classification of extensive medical data (eg
electroencephalography (EEG) signals) is a significant challenge to develop effective brain …
electroencephalography (EEG) signals) is a significant challenge to develop effective brain …
Sparse group representation model for motor imagery EEG classification
A potential limitation of a motor imagery (MI) based brain-computer interface (BCI) is that it
usually requires a relatively long time to record sufficient electroencephalogram (EEG) data …
usually requires a relatively long time to record sufficient electroencephalogram (EEG) data …
Motor imagery EEG signals decoding by multivariate empirical wavelet transform-based framework for robust brain–computer interfaces
The robustness and computational load are the key challenges in motor imagery (MI) based
on electroencephalography (EEG) signals to decode for the development of practical brain …
on electroencephalography (EEG) signals to decode for the development of practical brain …
EEG classification using sparse Bayesian extreme learning machine for brain–computer interface
Mu rhythm is a spontaneous neural response occurring during a motor imagery (MI) task
and has been increasingly applied to the design of brain–computer interface (BCI). Accurate …
and has been increasingly applied to the design of brain–computer interface (BCI). Accurate …