A comprehensive review of EEG-based brain–computer interface paradigms
Advances in brain science and computer technology in the past decade have led to exciting
developments in brain–computer interface (BCI), thereby making BCI a top research area in …
developments in brain–computer interface (BCI), thereby making BCI a top research area in …
EEG-based brain-computer interfaces using motor-imagery: Techniques and challenges
Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those
using motor-imagery (MI) data, have the potential to become groundbreaking technologies …
using motor-imagery (MI) data, have the potential to become groundbreaking technologies …
A multi-branch convolutional neural network with squeeze-and-excitation attention blocks for EEG-based motor imagery signals classification
Electroencephalography-based motor imagery (EEG-MI) classification is a critical
component of the brain-computer interface (BCI), which enables people with physical …
component of the brain-computer interface (BCI), which enables people with physical …
Personalized brain–computer interface and its applications
Y Ma, A Gong, W Nan, P Ding, F Wang… - Journal of Personalized …, 2022 - mdpi.com
Brain–computer interfaces (BCIs) are a new technology that subverts traditional human–
computer interaction, where the control signal source comes directly from the user's brain …
computer interaction, where the control signal source comes directly from the user's brain …
Electroencephalogram-based motor imagery signals classification using a multi-branch convolutional neural network model with attention blocks
GA Altuwaijri, G Muhammad - Bioengineering, 2022 - mdpi.com
Brain signals can be captured via electroencephalogram (EEG) and be used in various
brain–computer interface (BCI) applications. Classifying motor imagery (MI) using EEG …
brain–computer interface (BCI) applications. Classifying motor imagery (MI) using EEG …
A usability study of low-cost wireless brain-computer interface for cursor control using online linear model
Computer cursor control using electroencephalogram (EEG) signals is a common and well-
studied brain-computer interface (BCI). The emphasis of the literature has been primarily on …
studied brain-computer interface (BCI). The emphasis of the literature has been primarily on …
Perspectives on assistive systems for manual assembly tasks in industry
Small lot sizes in modern manufacturing present new challenges for people doing manual
assembly tasks. Assistive systems, including context-aware instruction systems and …
assembly tasks. Assistive systems, including context-aware instruction systems and …
A critical evaluation on low-cost consumer-grade electroencephalographic devices
S Pathirana, D Asirvatham… - 2018 2nd International …, 2018 - ieeexplore.ieee.org
Electroencephalography (EEG) has been recognized as one the finest cost-effective
techniques to measure the electrical activity of the human brain. Since the electrical activity …
techniques to measure the electrical activity of the human brain. Since the electrical activity …
Motor training using mental workload (MWL) with an assistive soft exoskeleton system: a functional near-infrared spectroscopy (fNIRS) study for brain–machine …
Mental workload is a neuroergonomic human factor, which is widely used in planning a
system's safety and areas like brain–machine interface (BMI), neurofeedback, and assistive …
system's safety and areas like brain–machine interface (BMI), neurofeedback, and assistive …
Toward human-centered shared autonomy AI paradigms for human-robot teaming in healthcare
With recent advancements in AI and computation tools, intelligent paradigms emerged to
empower different fields such as healthcare robots with new capabilities. Advanced AI …
empower different fields such as healthcare robots with new capabilities. Advanced AI …