Open AccessManifold decoding for neural representations of face viewpoint and gaze direction using magnetoencephalographic data
Author(s) -
Kuo PoChih,
Chen YongSheng,
Chen LiFen
Publication year - 2018
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.23998
Subject(s) - magnetoencephalography , gaze , decoding methods , neural decoding , superior temporal sulcus , autoencoder , computer science , artificial intelligence , representation (politics) , computer vision , encoding (memory) , psychology , face (sociological concept) , manifold (fluid mechanics) , pattern recognition (psychology) , perception , artificial neural network , neuroscience , electroencephalography , algorithm , mechanical engineering , social science , sociology , politics , political science , law , engineering
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.