Over the past decade, many researchers have come up with different implementations of
systems for decoding covert or imagined speech from EEG (electroencephalogram). They …

Electroencephalogram (EEG)-based brain–computer interfaces (BCIs) provide a novel
approach for controlling external devices. BCI technologies can be important enabling …

In recent years, advanced neurocomputing and machine learning techniques have been
used for Electroencephalogram (EEG)-based diagnosis of various neurological disorders. In …

Sleep disorder is a symptom of many neurological diseases that may significantly affect the
quality of daily life. Traditional methods are time-consuming and involve the manual scoring …

An important subfield of brain–computer interface is the classification of motor imagery (MI)
signals where a presumed action, for example, imagining the hands' motions, is mentally …

Surface electroencephalography is a standard and noninvasive way to measure electrical
brain activity. Recent advances in artificial intelligence led to significant improvements in the …

A direct-speech brain-computer interface (DS-BCI) acquires neural signals corresponding to
imagined speech, then processes and decodes these signals to produce a linguistic output …

Speech production is an intricate process involving a large number of muscles and cognitive
processes. The neural processes underlying speech production are not completely …

A tight coupling between the neuronal activity and the cerebral blood flow (CBF) is the
motivation of many hemodynamic response (HR)-based neuroimaging modalities. The …

Objective. Most brain–computer interfaces (BCIs) based on functional near-infrared
spectroscopy (fNIRS) require that users perform mental tasks such as motor imagery, mental …