AI Medical Compendium Topic:
Brain-Computer Interfaces

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A Hybrid Network for ERP Detection and Analysis Based on Restricted Boltzmann Machine.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Detecting and Please provide the correct one analyzing the event-related potential (ERP) remains an important problem in neuroscience. Due to the low signal-to-noise ratio and complex spatio-temporal patterns of ERP signals, conventional methods usua...

Robust Support Matrix Machine for Single Trial EEG Classification.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Electroencephalogram (EEG) signals are of complex structure and can be naturally represented as matrices. Classification is one of the most important steps for EEG signal processing. Newly developed classifiers can handle these matrix-form data by ad...

As above, so below? Towards understanding inverse models in BCI.

Journal of neural engineering
OBJECTIVE: In brain-computer interfaces (BCI), measurements of the user's brain activity are classified into commands for the computer. With EEG-based BCIs, the origins of the classified phenomena are often considered to be spatially localized in the...

Inferring imagined speech using EEG signals: a new approach using Riemannian manifold features.

Journal of neural engineering
OBJECTIVE: In this paper, we investigate the suitability of imagined speech for brain-computer interface (BCI) applications.

Training set extension for SVM ensemble in P300-speller with familiar face paradigm.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: P300-spellers are brain-computer interface (BCI)-based character input systems. Support vector machine (SVM) ensembles are trained with large-scale training sets and used as classifiers in these systems. However, the required large-scale ...

Augmenting intracortical brain-machine interface with neurally driven error detectors.

Journal of neural engineering
OBJECTIVE: Making mistakes is inevitable, but identifying them allows us to correct or adapt our behavior to improve future performance. Current brain-machine interfaces (BMIs) make errors that need to be explicitly corrected by the user, thereby con...

A brain-controlled lower-limb exoskeleton for human gait training.

The Review of scientific instruments
Brain-computer interfaces have been a novel approach to translate human intentions into movement commands in robotic systems. This paper describes an electroencephalogram-based brain-controlled lower-limb exoskeleton for gait training, as a proof of ...