Affective computing conjoins the research topics of emotion recognition and sentiment
analysis, and can be realized with unimodal or multimodal data, consisting primarily of …

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 …

In this paper, we present DREAMER, a multimodal database consisting of
electroencephalogram (EEG) and electrocardiogram (ECG) signals recorded during affect …

Automatic emotion recognition has become a trending research topic in the past decade.
While works based on facial expressions or speech abound, recognizing affect from body …

The omnipresence of numerous information sources () in our () daily lives () brings up new
alternatives () for emotion recognition in several domains including e-health, e-learning …

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

We introduce a new dataset for the emotional artificial intelligence research: identity-free
video dataset for micro-gesture understanding and emotion analysis (iMiGUE). Different …

Deep Learning has implemented a wide range of applications and has become increasingly
popular in recent years. The goal of multimodal deep learning (MMDL) is to create models …

Thanks to the decreasing cost of whole-body sensing technology and its increasing
reliability, there is an increasing interest in, and understanding of, the role played by body …

In this study, electroencephalography-based data for emotion recognition analysis are
introduced. EEG signals were collected from 28 different subjects with a wearable and …