Brain–computer interface for neurorehabilitation of upper limb after stroke

KK Ang, C Guan - Proceedings of the IEEE, 2015 - ieeexplore.ieee.org
Current rehabilitation therapies for stroke rely on physical practice (PP) by the patients.
Motor imagery (MI), the imagination of movements without physical action, presents an …

Correlation-based channel selection and regularized feature optimization for MI-based BCI

J Jin, Y Miao, I Daly, C Zuo, D Hu, A Cichocki - Neural Networks, 2019 - Elsevier
Multi-channel EEG data are usually necessary for spatial pattern identification in motor
imagery (MI)-based brain computer interfaces (BCIs). To some extent, signals from some …

Temporally constrained sparse group spatial patterns for motor imagery BCI

Y Zhang, CS Nam, G Zhou, J Jin… - IEEE transactions on …, 2018 - ieeexplore.ieee.org
Common spatial pattern (CSP)-based spatial filtering has been most popularly applied to
electroencephalogram (EEG) feature extraction for motor imagery (MI) classification in brain …

Spatio-spectral feature representation for motor imagery classification using convolutional neural networks

JS Bang, MH Lee, S Fazli, C Guan… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Convolutional neural networks (CNNs) have recently been applied to electroencephalogram
(EEG)-based brain–computer interfaces (BCIs). EEG is a noninvasive neuroimaging …

A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke

KK Ang, KSG Chua, KS Phua, C Wang… - Clinical EEG and …, 2015 - journals.sagepub.com
Electroencephalography (EEG)–based motor imagery (MI) brain-computer interface (BCI)
technology has the potential to restore motor function by inducing activity-dependent brain …

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 …

FBCNet: A multi-view convolutional neural network for brain-computer interface

R Mane, E Chew, K Chua, KK Ang, N Robinson… - arXiv preprint arXiv …, 2021 - arxiv.org
Lack of adequate training samples and noisy high-dimensional features are key challenges
faced by Motor Imagery (MI) decoding algorithms for electroencephalogram (EEG) based …

Deep convolutional neural networks for mental load classification based on EEG data

Z Jiao, X Gao, Y Wang, J Li, H Xu - Pattern Recognition, 2018 - Elsevier
Electroencephalograph (EEG), the representation of the brain's electrical activity, is a widely
used measure of brain activities such as working memory during cognitive tasks. Varying in …

Enhancing EEG-based classification of depression patients using spatial information

C Jiang, Y Li, Y Tang, C Guan - IEEE transactions on neural …, 2021 - ieeexplore.ieee.org
Background: Depression has become a leading mental disorder worldwide. Evidence has
shown that subjects with depression exhibit different spatial responses in …

Systematic Review of Single-Channel EEG-Based Drowsiness Detection Methods

VP Balam - IEEE Transactions on Intelligent Transportation …, 2024 - ieeexplore.ieee.org
Drowsiness is characterized by reduced attentiveness, commonly experienced during the
transition from wakefulness to sleepiness. It can decrease an individual's alertness, thereby …