Reconstruction of perceived face images from brain activities based on multi-attribute constraints.

ABSTRACT: Reconstruction of perceived faces from brain signals is a hot topic in brain decoding and an important application in the field of brain-computer interfaces. Existing methods do not fully consider the multiple facial attributes represented in face images, and their different activity patterns at multiple brain regions are often ignored, which causes the reconstruction performance very poor. In the current study, we propose an algorithmic framework that efficiently combines multiple face-selective brain regions for precise multi-attribute perceived face reconstruction. Our framework consists of three modules: a multi-task deep learning network (MTDLN), which is developed to simultaneously extract the multi-dimensional face features attributed to facial expression, identity and gender from o

SUBMITTER: Hou X 

PROVIDER: S-EPMC9643433 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature