title: Supervised learning for neural manifold using spatiotemporal brain activity.
abstract: OBJECTIVE
  Determining the means by which perceived stimuli are compactly represented in the human brain is a difficult task. This study aimed to develop techniques for the construction of the neural manifold as a representation of visual stimuli.
  
  
  METHODS
  We propose a supervised locally linear embedding method to construct the embedded manifold from brain activity, taking into account similarities between corresponding stimuli. In our experiments, photographic portraits were used as visual stimuli and brain activity was calculated from magnetoencephalographic data using a source localization method.
  
  
  RESULTS
  The results of 10 × 10-fold cross-validation revealed a strong correlation between manifolds of brain activity and the orientation of faces in the presented images, suggesting that high-level information related to image content can be revealed in the brain responses represented in the manifold.
  
  
  CONCLUSIONS
  Our experiments demonstrate that the proposed method is applicable to investigation into the inherent patterns of brain activity.
authors.affiliation: Department of Computer Science, National Chiao Tung University, 1001 University Road, Hsinchu, Taiwan.
authors.forename: Po-Chih
authors.initials: PC
authors.lastname: Kuo
authors.forename: Yong-Sheng
authors.initials: YS
authors.lastname: Chen
authors.forename: Li-Fen
authors.initials: LF
authors.lastname: Chen
mesh_terms: D000328:Adult; D001921:Brain / Q000502:physiology*; D005260:Female; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods*; D008297:Male; D009949:Orientation / Q000502:physiology*; D010775:Photic Stimulation / Q000379:methods*; D000069553:Supervised Machine Learning; D013997:Time Factors; D055815:Young Adult