Emotions are a critical aspect of daily life and serve a crucial role in human decision-making,
planning, reasoning, and other mental states. As a result, they are considered a significant …

In this study, we conducted a systematic literature review of 107 primary studies conducted
between 2017 and 2023 to discern trends in datasets, classifiers, and contributions to …

Deep learning has recently achieved remarkable success in emotion recognition based on
Electroencephalogram (EEG), in which convolutional neural networks (CNNs) are the mostly …

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 electroencephalogram (EEG) plays an increasingly important
role in the field of brain–computer interfaces. Recently, deep learning has been widely …

Downsampling operations such as max pooling or strided convolution are ubiquitously
utilized in Convolutional Neural Networks (CNNs) to aggregate local features, enlarge …

Recently, various deep learning frameworks have shown excellent performance in decoding
electroencephalogram (EEG) signals, especially in human emotion recognition. However …

How to extract discriminative latent feature representations from electroencephalography
(EEG) signals and build a generalized model is a topic in EEG-based emotion recognition …

Feature embeddings derived from continuous map** using the deep neural network are
critical for accurate classification in seizure prediction tasks. However, the embeddings of …

With the improvement of quality of life, people are more and more concerned about the
quality of sleep. The electroencephalogram (EEG)-based sleep stage classification is a …