Multi-modal conversation emotion recognition (MCER) aims to recognize and track the
speaker's emotional state using text, speech, and visual information in the conversation …

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

Multichannel electroencephalogram (EEG) is an array signal that represents brain neural
networks and can be applied to characterize information propagation patterns for different …

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

Emotion recognition is an essential but challenging task in human–computer interaction
systems due to the distinctive spatial structures and dynamic temporal dependencies …

Abstract Electroencephalography (EEG)-based brain-computer interfaces (BCIs) pose a
challenge for decoding due to their low spatial resolution and signal-to-noise ratio. Typically …

Electroencephalogram (EEG) is a crucial and widely utilized technique in neuroscience
research. In this paper, we introduce a novel graph neural network called the spatial …

Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is
challenging yet essential for decoding brain activities using brain-computer interfaces …

The close coupling of artificial intelligence (AI) and electroencephalography (EEG) has
substantially advanced human-computer interaction (HCI) technologies in the AI era …

Motor Imagery (MI) classification with electroencephalography (EEG) is a critical aspect of
Brain–Computer Interface (BCI) systems, enabling individuals with mobility limitations to …