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With the growing number of audio excerpts through various media and distribution channels, advanced audio analysis approaches have received significant interest in the multimedia field. However, current audio analysis approaches are still far from satisfactory due to the semantic gaps between the low-level acoustic features and high-level semantics perceived by human brain. In order to alleviate the...
Functional brain mapping under naturalistic stimuli such as video watching has been receiving greater interest in recent years. We presented a sparse representation based data-driven strategy to explore consistent functional brain networks during free viewing of continuous video streams. Compared with the traditional independent component analysis (ICA) based method, the novelty of our method is taking...
Mapping human brain networks has gained significant interest in the last few years, as it offers novel perspectives on the brain structure and function. However, most previous approaches were dedicated to a single resolution or scale of brain network, though the brain networks are multi-scale in nature. This paper presents a novel approach to constructing multi-scale structural brain networks from...
In this paper, we present a data-driven approach to explore consistent and discriminative functional interactions during free viewing of video stream. The underlying premise is that the functional interactions, derived from natural stimulus functional magnetic resonance imaging (N-fMRI) data of multiple subjects during watching videos in multi-categories, are simultaneously selected by multiple feature...
We present an algorithmic pipeline to assess the dynamics on human brain networks based on multimodal resting state functional magnetic resonance imaging (rsfMRI) and diffusion tensor imaging (DTI) data. We employ white matter fiber density information to parcellate the cerebral cortex into functionally homogenous regions, which are used as nodes to construct functional brain networks. Then, the dynamics...
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