4 accepted papers
Affective Brain-Computer Interfaces (aBCIs) have attracted growing attention due to their potential for decoding human emotional states through electroencephalogram (EEG) signals. However, existing deep learning models often struggle to fully capture both the spatial and temporal dependencies in EEG…
Emotion recognition through electroencephalography (EEG) has been an area of active research, but the inherent sensitivity of EEG signals to noise and artifacts poses significant challenges, especially in real-world settings. These complications often necessitate the removal of corrupted channels, m…
Multimodal emotion recognition based on electroencephalography (EEG) and eye movements has attracted increasing attention due to their high performance and complementary properties. However, there are two challenges that hinder its practical applications: the inconvenient EEG data collection and hig…
Electroencephalogram (EEG) has been applied in emotion recognition due to excellent temporal resolution with less competitive spatial resolution. This leads to the consequence that the majority of EEG-based emotion recognition models emphasize on exploiting temporal features while ignoring the effic…