A comprehensive review on critical issues and possible solutions of motor imagery based electroencephalography brain-computer interface
Motor imagery (MI) based brain–computer interface (BCI) aims to provide a means of
communication through the utilization of neural activity generated due to kinesthetic …
communication through the utilization of neural activity generated due to kinesthetic …
Empirical mode decomposition and its extensions applied to EEG analysis: a review
Empirical mode decomposition (EMD) provides an adaptive, data-driven approach to time–
frequency analysis, yielding components from which local amplitude, phase, and frequency …
frequency analysis, yielding components from which local amplitude, phase, and frequency …
Conversion and time-to-conversion predictions of mild cognitive impairment using low-rank affinity pursuit denoising and matrix completion
In this paper, we aim to predict conversion and time-to-conversion of mild cognitive
impairment (MCI) patients using multi-modal neuroimaging data and clinical data, via cross …
impairment (MCI) patients using multi-modal neuroimaging data and clinical data, via cross …
A hierarchical semi-supervised extreme learning machine method for EEG recognition
Feature extraction and classification is a vital part in motor imagery-based brain-computer
interface (BCI) system. Traditional deep learning (DL) methods usually perform better with …
interface (BCI) system. Traditional deep learning (DL) methods usually perform better with …
Semi-supervised learning quantization algorithm with deep features for motor imagery EEG Recognition in smart healthcare application
M Liu, M Zhou, T Zhang, N Xiong - Applied Soft Computing, 2020 - Elsevier
This paper depicts a novel semi-supervised classification model with convolutional neural
networks (CNN) for EEG Recognition. The performance of popular machine learning …
networks (CNN) for EEG Recognition. The performance of popular machine learning …
Sparse Representation‐Based Extreme Learning Machine for Motor Imagery EEG Classification
Q She, K Chen, Y Ma, T Nguyen… - Computational …, 2018 - Wiley Online Library
Classification of motor imagery (MI) electroencephalogram (EEG) plays a vital role in brain‐
computer interface (BCI) systems. Recent research has shown that nonlinear classification …
computer interface (BCI) systems. Recent research has shown that nonlinear classification …
Safe semi-supervised extreme learning machine for EEG signal classification
One major challenge in the current brain–computer interface research is the accurate
classification of time-varying electroencephalographic (EEG) signals. The labeled EEG …
classification of time-varying electroencephalographic (EEG) signals. The labeled EEG …
Balanced graph-based regularized semi-supervised extreme learning machine for EEG classification
Abstract Machine learning algorithms play a critical role in electroencephalograpy (EEG)-
based brain-computer interface (BCI) systems. However, collecting labeled samples for …
based brain-computer interface (BCI) systems. However, collecting labeled samples for …
Multi-class motor imagery EEG classification using collaborative representation-based semi-supervised extreme learning machine
Both labeled and unlabeled data have been widely used in electroencephalographic (EEG)-
based brain-computer interface (BCI). However, labeled EEG samples are generally scarce …
based brain-computer interface (BCI). However, labeled EEG samples are generally scarce …
Double‐Criteria Active Learning for Multiclass Brain‐Computer Interfaces
Q She, K Chen, Z Luo, T Nguyen… - Computational …, 2020 - Wiley Online Library
Recent technological advances have enabled researchers to collect large amounts of
electroencephalography (EEG) signals in labeled and unlabeled datasets. It is expensive …
electroencephalography (EEG) signals in labeled and unlabeled datasets. It is expensive …