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Brain-Computer Interfaces

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Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users.

PloS one
Motor Imagery Brain-Computer Interfaces (MI-BCIs) are AI-driven systems that capture brain activity patterns associated with mental imagination of movement and convert them into commands for external devices. Traditionally, MI-BCIs operate on Machine...

Spatio-Spectral Feature Representation for Motor Imagery Classification Using Convolutional Neural Networks.

IEEE transactions on neural networks and learning systems
Convolutional neural networks (CNNs) have recently been applied to electroencephalogram (EEG)-based brain-computer interfaces (BCIs). EEG is a noninvasive neuroimaging technique, which can be used to decode user intentions. Because the feature space ...

EEGSym: Overcoming Inter-Subject Variability in Motor Imagery Based BCIs With Deep Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
In this study, we present a new Deep Learning (DL) architecture for Motor Imagery (MI) based Brain Computer Interfaces (BCIs) called EEGSym. Our implementation aims to improve previous state-of-the-art performances on MI classification by overcoming ...

SSVEP-Based Brain Computer Interface Controlled Soft Robotic Glove for Post-Stroke Hand Function Rehabilitation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Soft robotic glove with brain computer interfaces (BCI) control has been used for post-stroke hand function rehabilitation. Motor imagery (MI) based BCI with robotic aided devices has been demonstrated as an effective neural rehabilitation tool to im...

Cross-Platform Implementation of an SSVEP-Based BCI for the Control of a 6-DOF Robotic Arm.

Sensors (Basel, Switzerland)
Robotics has been successfully applied in the design of collaborative robots for assistance to people with motor disabilities. However, man-machine interaction is difficult for those who suffer severe motor disabilities. The aim of this study was to ...

Noninvasive Human-Computer Interface Methods and Applications for Robotic Control: Past, Current, and Future.

Computational intelligence and neuroscience
The purpose of this study is to explore the noninvasive human-computer interaction methods that have been widely used in various fields, especially in the field of robot control. To have a deep understanding of the development of the methods, this pa...

Decoding neural activity preceding balance loss during standing with a lower-limb exoskeleton using an interpretable deep learning model.

Journal of neural engineering
Falls are a leading cause of death in adults 65 and older. Recent efforts to restore lower-limb function in these populations have seen an increase in the use of wearable robotic systems; however, fall prevention measures in these systems require ear...

Short report: surgery for implantable brain-computer interface assisted by robotic navigation system.

Acta neurochirurgica
We present an implantable brain-computer interface surgical case assisted by robotic navigation system in an elderly patient with tetraplegia caused by cervical spinal cord injury. Left primary motor cortex was selected for implantation of microelect...

A Bimodal Deep Learning Architecture for EEG-fNIRS Decoding of Overt and Imagined Speech.

IEEE transactions on bio-medical engineering
OBJECTIVE: Brain-computer interfaces (BCI) studies are increasingly leveraging different attributes of multiple signal modalities simultaneously. Bimodal data acquisition protocols combining the temporal resolution of electroencephalography (EEG) wit...

An MVMD-CCA Recognition Algorithm in SSVEP-Based BCI and Its Application in Robot Control.

IEEE transactions on neural networks and learning systems
This article proposes a novel recognition algorithm for the steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI) system. By combining the advantages of multivariate variational mode decomposition (MVMD) and canonical cor...