This article surveys the current state-of-the-art in affective computing (AC) principles,
methods, and tools as applied to games. We review this emerging field, namely affective …

Even with an unprecedented breakthrough of deep learning in electroencephalography
(EEG), collecting adequate labelled samples is a critical problem due to laborious and time …

Emotion recognition based on electroencephalography (EEG) signals has emerged as a
prominent research field, facilitating objective evaluation of diseases like depression and …

Affective computing based on electroencephalogram (EEG) has gained increasing attention
for its objectivity in measuring emotional states. While positive emotions play a crucial role in …

EEG emotion recognition plays a significant role in various mental health services. Deep
learning-based methods perform excellently, but still suffer from interpretability. Although …

The lack of complementary affective responses from both the central and peripheral nervous
systems could limit the performance of emotion recognition with the single-modal …

Extracting invariant representations in unlabeled electrocardiogram (ECG) signals is a
challenge for deep neural networks (DNNs). Contrastive learning is a promising method for …

Self-supervised approaches for electroencephalography (EEG) representation learning face
three specific challenges inherent to EEG data:(1) The low signal-to-noise ratio which …

Fatigue driving will seriously threaten public safety and health, so monitoring the brain's
cognitive state accurately and exploring the fatigue process is essential. This paper …

Due to inter-individual variances, cross-subject electroencephalogram (EEG)-based
emotion recognition is a challenging task. In this paper, we construct a multi-branch Capsule …