Brain–computer interfaces (BCIs) development is closely related to physics. In this paper, we
review the physical principles of BCIs, and underlying novel approaches for registration …

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

Brain signals refer to the biometric information collected from the human brain. The research
on brain signals aims to discover the underlying neurological or physical status of the …

Brain-Computer Interface (BCI) bridges human's neural world and the outer physical world
by decoding individuals' brain signals into commands recognizable by computer devices …

Objective. In recent years, brain–computer interface (BCI) systems based on
electroencephalography (EEG) have developed rapidly. However, the decoding of voluntary …

We conclude the review with an exercise in wishful thinking, which asks what the ideal
seizure prediction dataset would look like and how these data should be manipulated to …

Brain-computer interfaces (BCIs) have shown great prospects as real-time bidirectional links
between living brains and actuators. Artificial intelligence (AI), which can advance the …

Immersive virtual reality (VR) enables naturalistic neuroscientific studies while maintaining
experimental control, but dynamic and interactive stimuli pose methodological challenges …

Deep learning technology is uniquely suited to analyse neurophysiological signals such as
the electroencephalogram (EEG) and local field potentials (LFP) and promises to outperform …

Artificial neural networks (ANNs) are known to be a powerful tool for data analysis. They are
used in social science, robotics, and neurophysiology for solving tasks of classification …