Visual image reconstruction from human brain activity using a combination of multiscale local image decoders.
Perceptual experience consists of an enormous number of possible states. Previous fMRI studies have predicted a perceptual state by classifying brain activity into prespecified categories. Constraint-free visual image reconstruction is more challenging, as it is impractical to specify brain activity for all possible images. In this study, we reconstructed visual images by combining local image bases of multiple scales, whose contrasts were independently decoded from fMRI activity by automatically selecting relevant voxels and exploiting their correlated patterns. Binary-contrast, 10 x 10-patch images (2(100) possible states) were accurately reconstructed without any image prior on a single trial or volume basis by measuring brain activity only for several hundred random images. Reconstruction was also used to identify the presented image among millions of candidates. The results suggest that our approach provides an effective means to read out complex perceptual states from brain activity while discovering information representation in multivoxel patterns.
Miyawaki
Yoichi
Y
National Institute of Information and Communications Technology, Kyoto, Japan.
Uchida
Hajime
H
Yamashita
Okito
O
Sato
Masa-aki
MA
Morito
Yusuke
Y
Tanabe
Hiroki C
HC
Sadato
Norihiro
N
Kamitani
Yukiyasu
Y
D001921:Brain / Q000033:anatomy & histology / Q000098:blood supply* / Q000502:physiology*; D001931:Brain Mapping; D015350:Contrast Sensitivity / Q000502:physiology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010100:Oxygen / Q000097:blood; D010364:Pattern Recognition, Visual / Q000502:physiology; D010775:Photic Stimulation / Q000379:methods
D010100:Oxygen