Background Human emotions greatly affect the actions of a person. The automated emotion
recognition has applications in multiple domains such as health care, e-learning …

Nowadays, many deep models have been presented to recognize emotions using
electroencephalogram (EEG) signals. These deep models are computationally intensive, it …

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

The market uptake of Brain-Computer Interface technologies for clinical and non-clinical
applications is attracting the scientific world towards the development of daily-life wearable …

Electroencephalography (EEG) signals can demonstrate the activities of the human brain
and recognize different emotional states. Emotion recognition based on full EEG channels …

Emotion recognition based on electroencephalography (EEG) is of great importance in the
field of human–computer interaction. In recent years, deep learning methods, especially …

Introduction Efficiently recognizing emotions is a critical pursuit in brain–computer interface
(BCI), as it has many applications for intelligent healthcare services. In this work, an …

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

Within the field of Humanities, there is a recognized need for educational innovation, as
there are currently no reported tools available that enable individuals to interact with their …

Thanks to the advent of affective computing, designing an automatic human emotion
recognition system for clinical and non-clinical applications has attracted the attention of …