Using ECG signals captured by wearable devices for emotion recognition is a feasible
solution. We propose a deep convolutional neural network incorporating attentional …

Nowadays, human emotion recognition based on electrocardiogram (ECG) signal is
considered as a hot topic applied in many sensitive domains such as healthcare, social …

We present an electrocardiogram (ECG)-based emotion recognition system using self-
supervised learning. Our proposed architecture consists of two main networks, a signal …

Since electroencephalogram (EEG) signals can truly reflect human emotional state, emotion
recognition based on EEG has turned into a critical branch in the field of artificial …

Using electroencephalogram (EEG) signal to recognize emotional states has become a
research hotspot of affective computing. Previous emotion recognition methods almost …

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

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 …

In recent studies, machine learning and deep learning strategies have been explored in
many EEG-based application for best performance. More specifically, convolutional neural …

In this article, we propose a method for emotion classification based on multilayer
convolutional neural network (MCNN) and combining differential entropy (DE) and brain …

Abstract Emotion recognition using Artificial Intelligence (AI) is a fundamental prerequisite to
improve Human-Computer Interaction (HCI). Recognizing emotion from …