AIMC Topic: Brain-Computer Interfaces

Clear Filters Showing 501 to 510 of 703 articles

Parsing learning in networks using brain-machine interfaces.

Current opinion in neurobiology
Brain-machine interfaces (BMIs) define new ways to interact with our environment and hold great promise for clinical therapies. Motor BMIs, for instance, re-route neural activity to control movements of a new effector and could restore movement to pe...

Transfer Learning: A Riemannian Geometry Framework With Applications to Brain-Computer Interfaces.

IEEE transactions on bio-medical engineering
OBJECTIVE: This paper tackles the problem of transfer learning in the context of electroencephalogram (EEG)-based brain-computer interface (BCI) classification. In particular, the problems of cross-session and cross-subject classification are conside...

Deep learning with convolutional neural networks for EEG decoding and visualization.

Human brain mapping
Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end-to-end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end-to-end EEG analysis, but...

Virtual and Actual Humanoid Robot Control with Four-Class Motor-Imagery-Based Optical Brain-Computer Interface.

BioMed research international
Motor-imagery tasks are a popular input method for controlling brain-computer interfaces (BCIs), partially due to their similarities to naturally produced motor signals. The use of functional near-infrared spectroscopy (fNIRS) in BCIs is still emergi...

The impact of goal-oriented task design on neurofeedback learning for brain-computer interface control.

Medical & biological engineering & computing
Neurofeedback training teaches individuals to modulate brain activity by providing real-time feedback and can be used for brain-computer interface control. The present study aimed to optimize training by maximizing engagement through goal-oriented ta...

Automated Classification and Removal of EEG Artifacts With SVM and Wavelet-ICA.

IEEE journal of biomedical and health informatics
Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of EEG artifacts are frequently sought for clinical diagnostics and brain-computer interface applicati...

Object Extraction in Cluttered Environments via a P300-Based IFCE.

Computational intelligence and neuroscience
One of the fundamental issues for robot navigation is to extract an object of interest from an image. The biggest challenges for extracting objects of interest are how to use a machine to model the objects in which a human is interested and extract t...

Noninvasive Electroencephalogram Based Control of a Robotic Arm for Writing Task Using Hybrid BCI System.

BioMed research international
A novel hybrid brain-computer interface (BCI) based on the electroencephalogram (EEG) signal which consists of a motor imagery- (MI-) based online interactive brain-controlled switch, "teeth clenching" state detector, and a steady-state visual evoked...

Brain-Computer Interface application: auditory serial interface to control a two-class motor-imagery-based wheelchair.

Journal of neuroengineering and rehabilitation
BACKGROUND: Certain diseases affect brain areas that control the movements of the patients' body, thereby limiting their autonomy and communication capacity. Research in the field of Brain-Computer Interfaces aims to provide patients with an alternat...