Due to the limited perceptual field, convolutional neural networks (CNN) only extract local
temporal features and may fail to capture long-term dependencies for EEG decoding. In this …

The human digital twin (HDT) emerges as a promising human-centric technology in Industry
5.0, but challenges remain in human modeling and simulation. Digital human modeling …

Drowsiness is characterized by reduced attentiveness, commonly experienced during the
transition from wakefulness to sleepiness. It can decrease an individual's alertness, thereby …

Attention deficit hyperactivity disorder (ADHD) is one of the major psychiatric and
neurodevelopment disorders worldwide. Electroencephalography (EEG) signal-based …

Recently, electroencephalographic (EEG) emotion recognition attract attention in the field of
human-computer interaction (HCI). However, most of the existing EEG emotion datasets …

Emotion recognition using electroencephalogram signals has been widely studied in the last
decade, achieving artificial intelligence models that accurately classify primitive or primary …

Emotion Recognition through electroencephalography (EEG) is one of the prevailing
emotion recognition techniques achieving higher accuracy rates. Nevertheless, one of the …

During emotional changes, the brain generates many highly connected and highly
concentrated hub regions. Thus, barely studying the whole-brain network architecture while …

Most studies have demonstrated that EEG can be applied to emotion recognition. In the
process of EEG-based emotion recognition, real-time is an important feature. In this paper …

Negative emotions have been identified as significant factors influencing driver behavior,
easily leading to extremely serious traffic accidents. Hence, there is a pressing need to …