A brain computer interface (BCI) framework uses computer algorithms to detect mental
activity patterns and manipulate external devices. Because of its simplicity and non …

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 …

A brain–computer interface (BCI) can connect humans and machines directly and has
achieved successful applications in the past few decades. Many new BCI paradigms and …

The exploitation of brain signals for biometric recognition purposes has received significant
attention from the scientific community in the last decade, with most of the efforts so far …

Left and right hand motor imagery electroencephalogram (MI-EEG) signals are widely used
in brain-computer interface (BCI) systems to identify a participant intent in controlling …

Background: Brain-Computer Interface (BCI) is becoming more reliable, thanks to the
advantages of Artificial Intelligence (AI). Recently, hybrid Deep Learning (hDL), which …

Biomedical signals carry signature rhythms of complex physiological processes that control
our daily bodily activity. The properties of these rhythms indicate the nature of interaction …

Electroencephalogram (EEG) signals accumulate the brain's spiking activities using
standardized electrodes placed at the scalp. These cumulative brain signals are chaotic in …

Objective. Most deep neural networks (DNNs) used as brain computer interfaces (BCI)
classifiers are rarely viable for more than one person and are relatively shallow compared to …