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 …
Physical principles of brain–computer interfaces and their applications for rehabilitation, robotics and control of human brain states
Brain–computer interfaces (BCIs) development is closely related to physics. In this paper, we
review the physical principles of BCIs, and underlying novel approaches for registration …
review the physical principles of BCIs, and underlying novel approaches for registration …
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 …
Decoding natural reach-and-grasp actions from human EEG
Objective. Despite the high number of degrees of freedom of the human hand, most actions
of daily life can be executed incorporating only palmar, pincer and lateral grasp. In this study …
of daily life can be executed incorporating only palmar, pincer and lateral grasp. In this study …
A multibranch of convolutional neural network models for electroencephalogram-based motor imagery classification
Automatic high-level feature extraction has become a possibility with the advancement of
deep learning, and it has been used to optimize efficiency. Recently, classification methods …
deep learning, and it has been used to optimize efficiency. Recently, classification methods …
Electroencephalogram-based motor imagery signals classification using a multi-branch convolutional neural network model with attention blocks
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 …
Unimanual and bimanual reach-and-grasp actions can be decoded from human EEG
While most tasks of daily life can be handled through a small number of different grasps,
many tasks require the action of both hands. In these bimanual tasks, the second hand has …
many tasks require the action of both hands. In these bimanual tasks, the second hand has …
EEG patterns of self-paced movement imaginations towards externally-cued and internally-selected targets
In this study, we investigate the neurophysiological signature of the interacting processes
which lead to a single reach-and-grasp movement imagination (MI). While performing this …
which lead to a single reach-and-grasp movement imagination (MI). While performing this …
Exploring representations of human gras** in neural, muscle and kinematic signals
Movement covariates, such as electromyographic or kinematic activity, have been proposed
as candidates for the neural representation of hand control. However, it remains unclear …
as candidates for the neural representation of hand control. However, it remains unclear …
[HTML][HTML] Upper limb sensorimotor restoration through brain–computer interface technology in tetraparesis
M Bockbrader - Current Opinion in Biomedical Engineering, 2019 - Elsevier
For individuals with spinal cord injury, brain–computer interface (BCI) technology offers a
means to restore lost sensorimotor function by bridging disrupted neural pathways to …
means to restore lost sensorimotor function by bridging disrupted neural pathways to …