Recognition of emotions from physiological signals, and in particular from
electroencephalography (EEG), is a field within affective computing gaining increasing …

Electroencephalogram (EEG)-based emotion recognition has gained high attention in Brain-
Computer Interfaces. However, due to the non-linearity and non-stationarity of EEG signals …

As a physiological process and high-level cognitive behavior, emotion is an important
subarea in neuroscience research. Emotion recognition across subjects based on brain …

With the rapid development of Human-computer interaction, automatic emotion recognition
based on multichannel electroencephalography (EEG) signals has attracted much attention …

Emotion recognition based on electroencephalography (EEG) has received attention as a
way to implement human-centric services. However, there is still much room for …

Electroencephalogram (EEG) is a crucial and widely utilized technique in neuroscience
research. In this paper, we introduce a novel graph neural network called the spatial …

Visual modality is one of the most dominant modalities for current continuous emotion
recognition methods. Compared to which the EEG modality is relatively less sound due to its …

Scientists and researchers have tried to establish a bond between the emotions conveyed
and the subsequent mood perceived in a person. Emotions play a major role in terms of our …

In this paper, we propose a deep learning framework, TSception, for emotion detection from
electroencephalogram (EEG). TSception consists of temporal and spatial convolutional …

Electroencephalographic (EEG) recordings are receiving growing attention in the field of
emotion recognition, since they monitor the brain's first response to an external stimulus …