In recent years, the rapid advances in machine learning (ML) and information fusion has
made it possible to endow machines/computers with the ability of emotion understanding …

Brain-computer interfaces (BCIs) have undergone significant advancements in recent years.
The integration of deep learning techniques, specifically transformers, has shown promising …

Emotions composed of cognizant logical reactions toward various situations. Such mental
responses stem from physiological, cognitive, and behavioral changes …

Emotions have an important role in daily life, not only in human interaction, but also in
decision-making processes, and in the perception of the world around us. Due to the recent …

We present AMIGOS-A dataset for Multimodal research of affect, personality traits and mood
on Individuals and GrOupS. Different to other databases, we elicited affect using both short …

We present a multimodal data set for the analysis of human affective states. The
electroencephalogram (EEG) and peripheral physiological signals of 32 participants were …

Automatic emotion recognition is one of the most challenging tasks. To detect emotion from
nonstationary EEG signals, a sophisticated learning algorithm that can represent high‐level …

A novel method of using the spiking neural networks (SNNs) and the electroencephalograph
(EEG) processing techniques to recognize emotion states is proposed in this paper. Three …

This paper presents a user-independent emotion recognition method with the goal of
recovering affective tags for videos using electroencephalogram (EEG), pupillary response …

Human emotion recognition, crucial for interpersonal relations and human-building
interaction, identifies emotions from various behavioral signals to improve user interactions …