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 …
Critical issues in state-of-the-art brain–computer interface signal processing
DJ Krusienski, M Grosse-Wentrup… - Journal of neural …, 2011 - iopscience.iop.org
This paper reviews several critical issues facing signal processing for brain–computer
interfaces (BCIs) and suggests several recent approaches that should be further examined …
interfaces (BCIs) and suggests several recent approaches that should be further examined …
Spatio-spectral feature representation for motor imagery classification using convolutional neural networks
Convolutional neural networks (CNNs) have recently been applied to electroencephalogram
(EEG)-based brain–computer interfaces (BCIs). EEG is a noninvasive neuroimaging …
(EEG)-based brain–computer interfaces (BCIs). EEG is a noninvasive neuroimaging …
Mutual information-based selection of optimal spatial–temporal patterns for single-trial EEG-based BCIs
The common spatial pattern (CSP) algorithm is effective in decoding the spatial patterns of
the corresponding neuronal activities from electroencephalogram (EEG) signal patterns in …
the corresponding neuronal activities from electroencephalogram (EEG) signal patterns in …
An automatic subject specific intrinsic mode function selection for enhancing two-class EEG-based motor imagery-brain computer interface
The electroencephalogram (EEG) signals tend to have poor time-frequency localization
when analysis techniques involve a fixed set of basis functions such as in short-time Fourier …
when analysis techniques involve a fixed set of basis functions such as in short-time Fourier …
Quantum neural network-based EEG filtering for a brain–computer interface
A novel neural information processing architecture inspired by quantum mechanics and
incorporating the well-known Schrodinger wave equation is proposed in this paper. The …
incorporating the well-known Schrodinger wave equation is proposed in this paper. The …
An empirical mode decomposition based filtering method for classification of motor-imagery EEG signals for enhancing brain-computer interface
In this paper, we present a new filtering method based on the empirical mode decomposition
(EMD) for classification of motor imagery (MI) electroencephalogram (EEG) signals for …
(EMD) for classification of motor imagery (MI) electroencephalogram (EEG) signals for …
Decoding imagined 3D hand movement trajectories from EEG: evidence to support the use of mu, beta, and low gamma oscillations
Objective: To date, motion trajectory prediction (MTP) of a limb from non-invasive
electroencephalography (EEG) has relied, primarily, on band-pass filtered samples of EEG …
electroencephalography (EEG) has relied, primarily, on band-pass filtered samples of EEG …
[HTML][HTML] Brain-computer interface for persons with motor disabilities-A review
open-access license: This is an open access article distributed under the terms of the
Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which …
Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which …
EEG-based continuous control of a game using a 3 channel motor imagery BCI: BCI game
D Coyle, J Garcia, AR Satti… - 2011 IEEE Symposium …, 2011 - ieeexplore.ieee.org
This paper presents an overview of a multistage signal processing framework to tackle the
main challenges in continuous control protocols for motor imagery based synchronous and …
main challenges in continuous control protocols for motor imagery based synchronous and …