AIMC Topic: Brain-Computer Interfaces

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Data augmentation for deep-learning-based electroencephalography.

Journal of neuroscience methods Jul 31, 2020
BACKGROUND: Data augmentation (DA) has recently been demonstrated to achieve considerable performance gains for deep learning (DL)-increased accuracy and stability and reduced overfitting. Some electroencephalography (EEG) tasks suffer from low sampl...
Seizures Brain-Computer Interfaces Humans Deep Learning Machine Learning Electroencephalography
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The Effect of the Graphic Structures of Humanoid Robot on N200 and P300 Potentials.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jul 20, 2020
Humanoid robots are widely used in brain computer interface (BCI). Using a humanoid robot stimulus could increase the amplitude of event-related potentials (ERPs), which improves BCI performance. Since a humanoid robot contains many human elements, t...
Brain Robotics Brain-Computer Interfaces Photic Stimulation Electroencephalography Humans Evoked Potentials Event-Related Potentials, P300
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Spatial-Frequency Feature Learning and Classification of Motor Imagery EEG Based on Deep Convolution Neural Network.

Computational and mathematical methods in medicine Jul 20, 2020
EEG pattern recognition is an important part of motor imagery- (MI-) based brain computer interface (BCI) system. Traditional EEG pattern recognition algorithm usually includes two steps, namely, feature extraction and feature classification. In feat...
Models, Neurological Algorithms Brain-Computer Interfaces Humans Mathematical Concepts Pattern Recognition, Automated Electroencephalography Motor Cortex Neural Networks, Computer Databases, Factual Deep Learning Imagination
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Multi-class motor imagery EEG classification using collaborative representation-based semi-supervised extreme learning machine.

Medical & biological engineering & computing Jul 16, 2020
Both labeled and unlabeled data have been widely used in electroencephalographic (EEG)-based brain-computer interface (BCI). However, labeled EEG samples are generally scarce and expensive to collect, while unlabeled samples are considered to be abun...
Databases, Factual Support Vector Machine Least-Squares Analysis Neural Networks, Computer Algorithms Supervised Machine Learning Brain-Computer Interfaces Imagination Electroencephalography Benchmarking Humans Biomedical Engineering
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BCI training to move a virtual hand reduces phantom limb pain: A randomized crossover trial.

Neurology Jul 16, 2020
OBJECTIVE: To determine whether training with a brain-computer interface (BCI) to control an image of a phantom hand, which moves based on cortical currents estimated from magnetoencephalographic signals, reduces phantom limb pain.
Imagination Magnetoencephalography Phantom Limb Movement Motor Cortex Middle Aged Robotics Aged Cross-Over Studies Brain-Computer Interfaces Adult Humans Male Hand
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Expressing uncertainty in Human-Robot interaction.

PloS one Jul 16, 2020
Most people struggle to understand probability which is an issue for Human-Robot Interaction (HRI) researchers who need to communicate risks and uncertainties to the participants in their studies, the media and policy makers. Previous work showed tha...
Humans Risk Factors Robotics Probability Brain-Computer Interfaces
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Modeling of Human Operator Behavior for Brain-Actuated Mobile Robots Steering.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jul 15, 2020
Human operator control of brain-actuated robot steering based on electroencephalograph (EEG)-signals is a complex behavior consisting of surroundings perceiving, decision making, and commands issuing and differs among individual operators. However, n...
Brain-Computer Interfaces Electroencephalography Humans Brain Robotics
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Intra-cortical brain-machine interfaces for controlling upper-limb powered muscle and robotic systems in spinal cord injury.

Clinical neurology and neurosurgery Jul 15, 2020
OBJECTIVE: Intracortical brain-machine interface (iBMI) is an assistive strategy to restore lost sensorimotor function by bridging the disrupted neural pathways to reanimate paralyzed limbs. However, to date, none of the studies explored the trade-of...
Brain-Computer Interfaces Spinal Cord Injuries Electric Stimulation Therapy Upper Extremity Humans Robotics Artificial Limbs
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Deep learning-based BCI for gait decoding from EEG with LSTM recurrent neural network.

Journal of neural engineering Jul 13, 2020
OBJECTIVE: Mobile Brain/Body Imaging (MoBI) frameworks allowed the research community to find evidence of cortical involvement at walking initiation and during locomotion. However, the decoding of gait patterns from brain signals remains an open chal...
Electroencephalography Gait Brain-Computer Interfaces Neural Networks, Computer Deep Learning Humans
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Brain-Computer Interface-Based Humanoid Control: A Review.

Sensors (Basel, Switzerland) Jun 27, 2020
A Brain-Computer Interface (BCI) acts as a communication mechanism using brain signals to control external devices. The generation of such signals is sometimes independent of the nervous system, such as in Passive BCI. This is majorly beneficial for ...
Humans Machine Learning Algorithms Electroencephalography Brain Brain-Computer Interfaces Robotics
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