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Brain-Computer Interfaces

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Development and Application of Medicine-Engineering Integration in the Rehabilitation of Traumatic Brain Injury.

BioMed research international
The rapid progress of the combination of medicine and engineering provides better chances for the clinical treatment and healthcare engineering. Traumatic brain injury (TBI) and its related symptoms have become a major global health problem. At prese...

Benefits of deep learning classification of continuous noninvasive brain-computer interface control.

Journal of neural engineering
. Noninvasive brain-computer interfaces (BCIs) assist paralyzed patients by providing access to the world without requiring surgical intervention. Prior work has suggested that EEG motor imagery based BCI can benefit from increased decoding accuracy ...

Design of MRI structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals.

Scientific reports
This paper proposes a novel method and algorithms for the design of MRI structured personalized 3D spiking neural network models (MRI-SNN) for a better analysis, modeling, and prediction of EEG signals. It proposes a novel gradient-descent learning a...

An integrated deep learning model for motor intention recognition of multi-class EEG Signals in upper limb amputees.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Recognition of motor intention based on electroencephalogram (EEG) signals has attracted considerable research interest in the field of pattern recognition due to its notable application of non-muscular communication and con...

On Robot Compliance: A Cerebellar Control Approach.

IEEE transactions on cybernetics
The work presented here is a novel biological approach for the compliant control of a robotic arm in real time (RT). We integrate a spiking cerebellar network at the core of a feedback control loop performing torque-driven control. The spiking cerebe...

A Survey on Learning-Based Approaches for Modeling and Classification of Human-Machine Dialog Systems.

IEEE transactions on neural networks and learning systems
With the rapid development from traditional machine learning (ML) to deep learning (DL) and reinforcement learning (RL), dialog system equipped with learning mechanism has become the most effective solution to address human-machine interaction proble...

A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers.

Journal of neural engineering
Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by signal decoding. The emerging deep learning technique...

Robust and accurate decoding of hand kinematics from entire spiking activity using deep learning.

Journal of neural engineering
. Brain-machine interfaces (BMIs) seek to restore lost motor functions in individuals with neurological disorders by enabling them to control external devices directly with their thoughts. This work aims to improve robustness and decoding accuracy th...

The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning.

Sensors (Basel, Switzerland)
In addition to helping develop products that aid the disabled, brain-computer interface (BCI) technology can also become a modality of entertainment for all people. However, most BCI games cannot be widely promoted due to the poor control performance...

Motor imagery recognition with automatic EEG channel selection and deep learning.

Journal of neural engineering
Modern motor imagery (MI)-based brain computer interface systems often entail a large number of electroencephalogram (EEG) recording channels. However, irrelevant or highly correlated channels would diminish the discriminatory ability, thus reducing ...