Multiresolution analysis over simple graphs for brain computer interfaces

Multiresolution analysis over simple graphs for brain computer interfaces.

OBJECTIVE Multiresolution analysis (MRA) offers a useful framework for signal analysis in the temporal and spectral domains, although commonly employed MRA methods may not be the best approach for brain computer interface (BCI) applications. This study aims to develop a new MRA system for extracting tempo-spatial-spectral features for BCI applications based on wavelet lifting over graphs. APP...

Semi-Supervised Classification for Intracortical Brain-Computer Interfaces Semi-Supervised Classification for Intracortical Brain-Computer Interfaces

Intracortical brain-computer interface (BCI) systems may one day allow paralyzed patients to interface with robotic arms or computer programs using their thoughts alone. However, a common and unaddressed issue with these systems is that due to small instabilities in the recorded signals, the decoding algorithms they rely upon must be retrained daily in a supervised manner. While this may be acc...

Metabolic Brain-Computer Interfaces

Multimodal Brain-Computer Interfaces

A critical parameter of brain-computer interfaces (BCIs) is the number of dimensions a user can control independently. One way to increment this number without increasing the mental effort required to operate the system is to stimulate several sensory modalities simultaneously, and to distinguish brain activity patterns when the user focuses attention to different elements of this multisensory ...

Brain-computer interfaces.

Brain-computer interfaces (BCIs) are systems that give their users communication and control capabilities that do not depend on muscles. The user's intentions are determined from activity recorded by electrodes on the scalp, on the cortical surface, or within the brain. BCIs can enable people who are paralyzed by amyotrophic lateral sclerosis (ALS), brainstem stroke, or other disorders to conve...