Emotions are an essential part of daily human communication. The emotional states and
dynamics of the brain can be linked by electroencephalography (EEG) signals that can be …

Emotion recognition from Electroencephalography (EEG) is proved to be a good choice as it
cannot be mimicked like speech signals or facial expressions. EEG signals of emotions are …

Emotions are closely related to human behavior, family and society. Since changes in
emotions can cause differences in electroencephalography (EEG) signals, EEG signals can …

Modern deep learning holds a great potential to transform clinical studies of human sleep.
Teaching a machine to carry out routine tasks would be a tremendous reduction in workload …

EEG signals have been reported to be informative and reliable for emotion recognition in
recent years. However, the inter-subject variability of emotion-related EEG signals still poses …

The high temporal resolution and the asymmetric spatial activations are essential attributes
of electroencephalogram (EEG) underlying emotional processes in the brain. To learn the …

In this paper, we present a novel method, called four-dimensional convolutional recurrent
neural network, which integrating frequency, spatial and temporal information of …

Objective. Previous studies on emotion recognition from electroencephalography (EEG)
mainly rely on single-channel-based feature extraction methods, which ignore the functional …

Emotional recognition contributes to automatically perceive the user's emotional response to
multimedia content through implicit annotation, which further benefits establishing effective …

Objective. The data scarcity problem in emotion recognition from electroencephalography
(EEG) leads to difficulty in building an affective model with high accuracy using machine …