The vast proliferation of sensor devices and Internet of Things enables the applications of
sensor-based activity recognition. However, there exist substantial challenges that could …

Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic
oscillations of neural activity, which is one of the core technologies of brain-computer …

Emotion recognition based on electroencephalography (EEG) is a significant task in the
brain-computer interface field. Recently, many deep learning-based emotion recognition …

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

Recently, deep neural networks (DNNs) have been applied to emotion recognition tasks
based on electroencephalography (EEG), and have achieved better performance than …

At present, people usually use some methods based on convolutional neural networks
(CNNs) for Electroencephalograph (EEG) decoding. However, CNNs have limitations in …

Emotion is interpreted as a psycho-physiological process, and it is associated with
personality, behavior, motivation, and character of a person. The objective of affective …

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

Motor imagery classification from EEG signals is essential for motor rehabilitation with a
Brain-Computer Interface (BCI). Most current works on this issue require a subject-specific …

Recently, the combination of neural network and attention mechanism is widely employed
for electroencephalogram (EEG) emotion recognition (EER) and has achieved remarkable …