Background and objective: Emotion classification tasks based on electroencephalography
(EEG) are an essential part of artificial intelligence, with promising applications in healthcare …

Collecting emotional physiological signals is significant in building affective Human-
Computer Interactions (HCI). However, how to evoke subjects' emotions efficiently in EEG …

Encountering unexpected events with different levels of danger can cause different levels of
emotional changes in the driver, and identifying the driver's mental state can assist in …

Phasor measurement units (PMUs) are being widely installed on power systems, providing a
unique opportunity to enhance wide-area situational awareness. One essential application …

Electroencephalogram (EEG) brain network embodies the brain's coordination and
interaction mechanism, and the transformations of emotional states are usually …

Benefiting from the high-temporal resolution of electroencephalogram (EEG), EEG-based
emotion recognition has become one of the hotspots of affective computing. For EEG-based …

The increasing amount and complexity of clinical data require an appropriate way of storing
and analyzing those data. Traditional approaches use a tabular structure (relational …

Neuroscience research reveals that different emotions are associated with different
functional connectivity structures of brain regions. However, many existing EEG-based …

Hand gesture and movement analysis is a crucial learning task in Human-computer
interaction (HCI) applications. Sensor-based HCI systems simultaneously capture the …

Deep Neural Network (DNN) is an advancing technology that improves our life by allowing
machines to perform complex tasks. Hybrid Deep Neural Network (HDNN) is widely used for …