Multi-view Multi-label Fine-grained Emotion Decoding from Human Brain Activity

1 code implementation • 26 Oct 2022 • Kaicheng Fu, Changde Du, Shengpei Wang, Huiguang He

Existing emotion decoding methods still have two main limitations: one is only decoding a single emotion category from a brain activity pattern and the decoded emotion categories are coarse-grained, which is inconsistent with the complex emotional expression of human; the other is ignoring the discrepancy of emotion expression between the left and right hemispheres of human brain.

Multi-Label Classification

Decoding Visual Neural Representations by Multimodal Learning of Brain-Visual-Linguistic Features

2 code implementations • 13 Oct 2022 • Changde Du, Kaicheng Fu, Jinpeng Li, Huiguang He

Finally, we construct three trimodal matching datasets, and the extensive experiments lead to some interesting conclusions and cognitive insights: 1) decoding novel visual categories from human brain activity is practically possible with good accuracy; 2) decoding models using the combination of visual and linguistic features perform much better than those using either of them alone; 3) visual perception may be accompanied by linguistic influences to represent the semantics of visual stimuli.