標題: | Decoding and encoding of visual patterns using magnetoencephalographic data represented in manifolds |
作者: | Kuo, Po-Chih Chen, Yong-Sheng Chen, Li-Fen Hsieh, Jen-Chuen 分子醫學與生物工程研究所 資訊工程學系 Institute of Molecular Medicine and Bioengineering Department of Computer Science |
關鍵字: | Visual decoding;Visual encoding;Magnetoencephalography;Manifold |
公開日期: | 15-十一月-2014 |
摘要: | Visual decoding and encoding are crucial aspects in investigating the representation of visual information in the human brain. This paper proposes a bidirectional model for decoding and encoding of visual stimulus based on manifold representation of the temporal and spatial information extracted from magnetoencephalographic data. In the proposed decoding process, principal component analysis is applied to extract temporal principal components (TPCs) from the visual cortical activity estimated by a beamforming method. The spatial distribution of each TPC is in a high-dimensional space and can be mapped to the corresponding spatiotemporal component (STC) on a low-dimensional manifold. Once the linear mapping between the STC and the wavelet coefficients of the stimulus image is determined, the decoding process can synthesize an image resembling the stimulus image. The encoding process is performed by reversing the mapping or transformation in the decoding model and can predict the spatiotemporal brain activity from a stimulus image. In our experiments using visual stimuli containing eleven combinations of checkerboard patches, the information of spatial layout in the stimulus image was revealed in the embedded manifold. The correlation between the reconstructed and original images was 0.71 and the correlation map between the predicted and original brain activity was highly correlated to the map between the original brain activity for different stimuli (r = 0.89). These results suggest that the temporal component is important in visual processing and manifolds can well represent the information related to visual perception. (c) 2014 Published by Elsevier Inc. |
URI: | http://dx.doi.org/10.1016/j.neuroimage.2014.07.046 http://hdl.handle.net/11536/123902 |
ISSN: | 1053-8119 |
DOI: | 10.1016/j.neuroimage.2014.07.046 |
期刊: | NEUROIMAGE |
Volume: | 102 |
起始頁: | 435 |
結束頁: | 450 |
顯示於類別: | 期刊論文 |