The electroencephalogram (EEG) has great attraction in emotion recognition studies due to
its resistance to deceptive actions of humans. This is one of the most significant advantages …

Given the rapid development of brain–computer interfaces, emotion identification based on
EEG signals has emerged as a new study area with tremendous importance in recent years …

Automatic electroencephalogram (EEG) emotion recognition is a challenging component of
human–computer interaction (HCI). Inspired by the powerful feature learning ability of …

Currently, Fourier-based, wavelet-based, and Hilbert-based time–frequency techniques
have generated considerable interest in classification studies for emotion recognition in …

The existed methods for electroencephalograph (EEG) emotion recognition always train the
models based on all the EEG samples indistinguishably. However, some of the source …

Recently, electroencephalogram-based emotion recognition has become crucial in enabling
the Human-Computer Interaction (HCI) system to become more intelligent. Due to the …

Electroencephalogram (EEG) based emotional analysis has been employed in medical
science, security and human–computer interaction with good success. In the recent past …

Human emotion recognition could greatly contribute to human–computer interaction with
promising applications in artificial intelligence. One of the challenges in recognition tasks is …

The temporal and spatial information of electroencephalogram (EEG) are essential for the
emotion recognition model to learn the discriminative features. Hence, we propose a novel …

Advances in signal processing and machine learning have expedited
electroencephalogram (EEG)-based emotion recognition research, and numerous EEG …