Objective. Most current electroencephalography (EEG)-based brain–computer interfaces
(BCIs) are based on machine learning algorithms. There is a large diversity of classifier …

Recent technological progress has allowed the development of low-cost and highly portable
brain sensors such as pre-amplified dry-electrodes to measure cognitive activity out of the …

The fields of music, health, and technology have seen significant interactions in recent years
in develo** music technology for health care and well-being. In an effort to strengthen the …

In this chapter, we present a state of the art on Brain-Computer Interface (BCI) use in
contemporary art. We analyzed sixty-one artworks that employ BCI dating from 1965 to …

Cognitive workload is one of the widely invoked human factors in the areas of human–
machine interaction (HMI) and neuroergonomics. The precise assessment of cognitive and …

Research in brain-computer interfaces has achieved impressive progress towards
implementing assistive technologies for restoration or substitution of lost motor capabilities …

Brain–computer interfaces (BCI) can extract information about the subject's intentions by
registering and processing electroencephalographic (EEG) signals to generate actions on …

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 …

In this work, an algorithm for the classification of six motor functions from an
electroencephalogram (EEG) signal that combines a common spatial pattern (CSP) filter and …

Brain–Computer Interfaces (BCIs) have become increasingly popular in recent years due to
their potential applications in diverse fields, ranging from the medical sector (people with …