In recent years, electroencephalography (EEG) emotion recognition has achieved excellent
progress. However, the applied shallow convolutional neural networks (CNNs) cannot …

Deep learning techniques have drawn considerable interest in emotion recognition due to
recent technological developments in healthcare analytics. Automatic patient emotion …

The identification of compound fault components of a planetary gearbox is especially
important for keeping the mechanical equipment working safely. However, the recognition …

Graph neural networks (GNNs) have demonstrated efficient processing of graph-structured
data, making them a promising method for electroencephalogram (EEG) emotion …

Due to the severe cross-subject data variations of electroencephalography (EEG) signals,
the issue of subject-independent EEG-based emotion recognition remains challenging till …

The gated recurrent unit (GRU) network can effectively capture temporal information for 1D
signals, such as electroencephalography and event-related brain potential, and it has been …

The classification of mental or cognitive tasks in real time using single-or multi-channel EEG
signals is an important field of research for neurofeedback and portable brain–computer …

Nowadays, everything needs to be digitized, and scientific knowledge is constantly bringing
comfort and change to everyday life. Autonomous systems have become a prominent …

Driver fatigue continues to be a major contributor to road traffic accidents, significantly
compromising a driver's ability to safely operate a vehicle. Existing fatigue driving detection …