Decades of research have shown machine learning superiority in discovering highly
nonlinear patterns embedded in electroencephalography (EEG) records compared with …

Electroencephalography (EEG) has countless applications across many of fields. However,
EEG applications are limited by low signal-to-noise ratios. Multiple types of artifacts …

Electroencephalography (EEG) is used in the diagnosis, monitoring, and prognostication of
many neurological ailments including seizure, coma, sleep disorders, brain injury, and …

Electroencephalogram (EEG) excels in portraying rapid neural dynamics at the level of
milliseconds, but its spatial resolution has often been lagging behind the increasing …

During the acquisition of electroencephalographic (EEG) signals, various factors can
influence the data and lead to the presence of one or multiple bad channels. Bad channel …

Acquisition of neuronal signals involves a wide range of devices with specific electrical
properties. Combined with other physiological sources within the body, the signals sensed …

Recently, due to the active expansion of the Internet of Things (IoT) and Ubiquitous
Computing, the neuro-augmented methods and tools for controlling software systems are on …

Objective. Electroencephalogram (EEG) recordings often contain large segments with
missing signals due to poor electrode contact or other artifact contamination. Recovering …

Consumer-grade Electroencephalography (EEG) devices equipped with few electrodes
often suffer from low spatial resolution, hindering the accurate capture of intricate brain …

Electroencephalography (EEG) signals are usually affected by presence of missing data
because of various reasons. Depending on the percentage of missing data, it affects …