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EEG-based Emotion Style Transfer Network for Cross-dataset Emotion Recognition
The transfer module encodes the domain-specific information of source and target domains and then re-constructs the source domain's emotional pattern and the target domain's statistical characteristics into the new stylized EEG representations.
Progressive Graph Convolution Network for EEG Emotion Recognition
Moreover, motivated by the observation of the relationship between coarse- and fine-grained emotions, we adopt a dual-head module that enables the PGCN to progressively learn more discriminative EEG features, from coarse-grained (easy) to fine-grained categories (difficult), referring to the hierarchical characteristic of emotion.
Quantum-limited Localisation and Resolution in Three Dimensions
As a method to extract information from optical system, imaging can be viewed as a parameter estimation problem.
Quantum Physics Optics
