AIMC Topic: Brain-Computer Interfaces

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Brain-Computer Interface for Control of Wheelchair Using Fuzzy Neural Networks.

BioMed research international
The design of brain-computer interface for the wheelchair for physically disabled people is presented. The design of the proposed system is based on receiving, processing, and classification of the electroencephalographic (EEG) signals and then perfo...

Maze learning by a hybrid brain-computer system.

Scientific reports
The combination of biological and artificial intelligence is particularly driven by two major strands of research: one involves the control of mechanical, usually prosthetic, devices by conscious biological subjects, whereas the other involves the co...

A Magnetic Resonance Compatible Soft Wearable Robotic Glove for Hand Rehabilitation and Brain Imaging.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
In this paper, we present the design, fabrication and evaluation of a soft wearable robotic glove, which can be used with functional Magnetic Resonance imaging (fMRI) during the hand rehabilitation and task specific training. The soft wearable roboti...

A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Motor imagery classification is an important topic in brain-computer interface (BCI) research that enables the recognition of a subject's intension to, e.g., implement prosthesis control. The brain dynamics of motor imagery are usually measured by el...

Thought-Controlled Nanoscale Robots in a Living Host.

PloS one
We report a new type of brain-machine interface enabling a human operator to control nanometer-size robots inside a living animal by brain activity. Recorded EEG patterns are recognized online by an algorithm, which in turn controls the state of an e...

The Role of Audio-Visual Feedback in a Thought-Based Control of a Humanoid Robot: A BCI Study in Healthy and Spinal Cord Injured People.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The efficient control of our body and successful interaction with the environment are possible through the integration of multisensory information. Brain-computer interface (BCI) may allow people with sensorimotor disorders to actively interact in th...

Anticipation: Beyond synthetic biology and cognitive robotics.

Bio Systems
The aim of this paper is to propose that current robotic technologies cannot have intentional states any more than is feasible within the sensorimotor variant of embodied cognition. It argues that anticipation is an emerging concept that can provide ...

Driving a Semiautonomous Mobile Robotic Car Controlled by an SSVEP-Based BCI.

Computational intelligence and neuroscience
Brain-computer interfaces represent a range of acknowledged technologies that translate brain activity into computer commands. The aim of our research is to develop and evaluate a BCI control application for certain assistive technologies that can be...

Control of Redundant Kinematic Degrees of Freedom in a Closed-Loop Brain-Machine Interface.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Brain-machine interface (BMI) systems use signals acquired from the brain to directly control the movement of an actuator, such as a computer cursor or a robotic arm, with the goal of restoring motor function lost due to injury or disease of the nerv...

Motor Imagery Classification Based on Bilinear Sub-Manifold Learning of Symmetric Positive-Definite Matrices.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
In motor imagery brain-computer interfaces (BCIs), the symmetric positive-definite (SPD) covariance matrices of electroencephalogram (EEG) signals carry important discriminative information. In this paper, we intend to classify motor imagery EEG sign...