Emotion recognition is the ability to precisely infer human emotions from numerous sources
and modalities using questionnaires, physical signals, and physiological signals. Recently …

In recent years, the rapid development of sensors and information technology has made it
possible for machines to recognize and analyze human emotions. Emotion recognition is an …

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

Since 2021, the term" Metaverse" has been the most popular one, garnering a lot of interest.
Because of its contained environment and built-in computing and networking capabilities, a …

The use of machine learning (ML) techniques in affective computing applications focuses on
improving the user experience in emotion recognition. The collection of input data (eg …

Giving emotional intelligence to machines can facilitate the early detection and prediction of
mental diseases and symptoms. Electroencephalography (EEG)-based emotion recognition …

In recent image classification approaches, a vision transformer (ViT) has shown an excellent
performance beyond that of a convolutional neural network. A ViT achieves a high …

Machine Learning (ML) offers unique and powerful tools for mental health practitioners to
improve evidence-based psychological interventions and diagnoses. Indeed, by detecting …

In recent literature, ECG-based stress assessment has become popular due to its proven
correlation to stress and increased accessibility of ECG data through commodity hardware …

With advances in portable and wearable devices, it should be possible to analyze and
interpret the collected biosignals from those devices to tailor a psychological intervention to …