Interpretable EEG Emotion Classification via CNN Model and Gradient-Weighted Class Activation Mapping.

Electroencephalography (EEG)-based emotion recognition plays an important role in affective computing and brain-computer interface applications. However, existing methods often face the challenge of achieving high classification accuracy while maintaining physiological interpretability. In this study, we propose a convolutional neural network (CNN) model with a simple architecture for EEG-based emotion classification.

Modality-general representations of valences perceived from visual and auditory modalities.

Valence is a dimension of emotion and can be either positive, negative, or neutral. Valences can be expressed through the visual and auditory modalities, and the valences of each modality can be conveyed by different types of stimuli (face, body, voice or music). This study focused on the modality-general representations of valences, that is, valence information can be shared across not only visual and auditory modalities but also different types of stimuli within each modality.

Abstract Representations of Emotions Perceived From the Face, Body, and Whole-Person Expressions in the Left Postcentral Gyrus.

Emotions can be perceived through the face, body, and whole-person, while previous studies on the abstract representations of emotions only focused on the emotions of the face and body. It remains unclear whether emotions can be represented at an abstract level regardless of all three sensory cues in specific brain regions. In this study, we used the representational similarity analysis (RSA) to explore the hypothesis that the emotion category is independent of all three stimulus types and can be decoded based on the activity patterns elicited by different emotions.

Linear Representation of Emotions in Whole Persons by Combining Facial and Bodily Expressions in the Extrastriate Body Area.

Our human brain can rapidly and effortlessly perceive a person's emotional state by integrating the isolated emotional faces and bodies into a whole. Behavioral studies have suggested that the human brain encodes whole persons in a holistic rather than part-based manner. Neuroimaging studies have also shown that body-selective areas prefer whole persons to the sum of their parts.

Revealing the Semantic Association between Perception of Scenes and Significant Objects by Representational Similarity Analysis.

'Significant' objects contribute greatly to scene recognition. The lateral occipital complex (LOC), parahippocampal place area (PPA), and retrosplenial cortex (RSC) play a crucial role in the cognitive processing of objects and scenes. However, the associated mechanism between objects and scenes remains unclear.