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Long short-term memory-based neural decoding of object categories evoked by natural images.

ABSTRACT: Visual perceptual decoding is one of the important and challenging topics in cognitive neuroscience. Building a mapping model between visual response signals and visual contents is the key point of decoding. Most previous studies used peak response signals to decode object categories. However, brain activities measured by functional magnetic resonance imaging are a dynamic process with time dependence, so peak signals cannot fully represent the whole process, which may affect the performance of decoding. Here, we propose a decoding model based on long short-term memory (LSTM) network to decode five object categories from multitime response signals evoked by natural images. Experimental results show that the average decoding accuracy using the multitime (2-6 s) response signals is 0.540 from the five subjects, which is significantly higher than that using the peak ones (6 s; accuracy: 0.492; p

SUBMITTER: Huang W

PROVIDER: S-EPMC7502843 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Long short-term memory-based neural decoding of object categories evoked by natural images.

Huang Wei W Yan Hongmei H Wang Chong C Li Jiyi J Yang Xiaoqing X Li Liang L Zuo Zhentao Z Zhang Jiang J Chen Huafu H

Human brain mapping 20200710 15

Visual perceptual decoding is one of the important and challenging topics in cognitive neuroscience. Building a mapping model between visual response signals and visual contents is the key point of decoding. Most previous studies used peak response signals to decode object categories. However, brain activities measured by functional magnetic resonance imaging are a dynamic process with time dependence, so peak signals cannot fully represent the whole process, which may affect the performance of ...[more]

PMID: 32648632

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