Supervised Learning for Neural Manifold Using Spatiotemporal Brain Activity

Symposium 1-2Time:13:30 - 15:00

Yong-Sheng Chen1
1Department of Computer Science, National Chiao Tung University, Taiwan,

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. 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. 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. Our experiments demonstrate that the proposed method is applicable to investigation into the inherent patterns of brain activity. (Coauthored with Po-Chih Kuo and Li-Fen Chen) Keywords: supervised learning, manifold, locally linear embedding, MEG, face orientation.

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December 10, 2016

Symposia submissions due:
March 1, 2017

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April 10, 2017

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May 20-22, 2017

Registration open:
May 21, 2017

September 1-3, 2017