Status of deep learning for EEG-based brain–computer interface applications
In the previous decade, breakthroughs in the central nervous system bioinformatics and
computational innovation have prompted significant developments in brain–computer …
computational innovation have prompted significant developments in brain–computer …
An EEG channel selection method for motor imagery based brain–computer interface and neurofeedback using Granger causality
H Varsehi, SMP Firoozabadi - Neural Networks, 2021 - Elsevier
Motor imagery (MI) brain–computer interface (BCI) and neurofeedback (NF) with
electroencephalogram (EEG) signals are commonly used for motor function improvement in …
electroencephalogram (EEG) signals are commonly used for motor function improvement in …
Epileptic seizure detection in EEGs signals using a fast weighted horizontal visibility algorithm
This paper proposes a fast weighted horizontal visibility graph constructing algorithm
(FWHVA) to identify seizure from EEG signals. The performance of the FWHVA is evaluated …
(FWHVA) to identify seizure from EEG signals. The performance of the FWHVA is evaluated …
A matrix determinant feature extraction approach for decoding motor and mental imagery EEG in subject-specific tasks
This study introduces a novel matrix determinant feature extraction approach for efficient
classification of motor and mental imagery activities from electroencephalography (EEG) …
classification of motor and mental imagery activities from electroencephalography (EEG) …
Neural network-based three-class motor imagery classification using time-domain features for BCI applications
Many studies have reported the usefulness of motor imagery (MI) electroencephalogram
(EEG) signals for Brain Computer Interface (BCI) systems. MI has been broadly …
(EEG) signals for Brain Computer Interface (BCI) systems. MI has been broadly …
Assessment of instantaneous cognitive load imposed by educational multimedia using electroencephalography signals
R Sarailoo, K Latifzadeh, SH Amiri… - Frontiers in …, 2022 - frontiersin.org
The use of multimedia learning is increasing in modern education. On the other hand, it is
crucial to design multimedia contents that impose an optimal amount of cognitive load …
crucial to design multimedia contents that impose an optimal amount of cognitive load …
Identification of resting and active state EEG features of Alzheimer's disease using discrete wavelet transform
P Ghorbanian, DM Devilbiss, A Verma… - Annals of biomedical …, 2013 - Springer
Alzheimer's disease (AD) is associated with deficits in a number of cognitive processes and
executive functions. Moreover, abnormalities in the electroencephalogram (EEG) power …
executive functions. Moreover, abnormalities in the electroencephalogram (EEG) power …
[HTML][HTML] Hospitalization status and gender recognition over the arboviral medical records using shallow and RNN-based deep models
In global health systems, clinicians have a challenging decision of a triage patient exposed
to arbovirus infections to determine they should be hospitalized. Diagnosing symptoms and …
to arbovirus infections to determine they should be hospitalized. Diagnosing symptoms and …
Species-Level Microfossil Prediction for Globotruncana genus Using Machine Learning Models
Microfossils provide evidence and knowledge about the Earth, life, ecology, and biological
changes, and one way to access this knowledge is through the classification of microfossil …
changes, and one way to access this knowledge is through the classification of microfossil …
Discrete wavelet transform EEG features of Alzheimer's disease in activated states
P Ghorbanian, DM Devilbiss, AJ Simon… - … Conference of the …, 2012 - ieeexplore.ieee.org
In this study, electroencephalogram (EEG) signals obtained by a single-electrode device
from 24 subjects-10 with Alzheimer's disease (AD) and 14 age-matched Controls (CN)-were …
from 24 subjects-10 with Alzheimer's disease (AD) and 14 age-matched Controls (CN)-were …