Manifold decoding for neural representations of face viewpoint and gaze direction using magnetoencephalographic data

Po Chih Kuo, Yong-Sheng Chen*, Li Fen Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

The main challenge in decoding neural representations lies in linking neural activity to representational content or abstract concepts. The transformation from a neural-based to a low-dimensional representation may hold the key to encoding perceptual processes in the human brain. In this study, we developed a novel model by which to represent two changeable features of faces: face viewpoint and gaze direction. These features are embedded in spatiotemporal brain activity derived from magnetoencephalographic data. Our decoding results demonstrate that face viewpoint and gaze direction can be represented by manifold structures constructed from brain responses in the bilateral occipital face area and right superior temporal sulcus, respectively. Our results also show that the superposition of brain activity in the manifold space reveals the viewpoints of faces as well as directions of gazes as perceived by the subject. The proposed manifold representation model provides a novel opportunity to gain further insight into the processing of information in the human brain.

Original languageEnglish
Pages (from-to)2191-2209
Number of pages19
JournalHuman Brain Mapping
Volume39
Issue number5
DOIs
StatePublished - 1 May 2018

Keywords

  • decoding
  • face viewpoint
  • gaze direction
  • manifold
  • MEG
  • neural representation
  • OFA
  • STS

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