Peculiarity Oriented Multi-Aspect Brain Data Analysis for Studying Human Multi-Perception Mechanism

Ning Zhong, S. Motomura, Jing-long Wu
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引用次数: 7

Abstract

In order to investigate the structure of advanced human brain activities, various brain analysis methods are required. It has been observed that multiple brain data such as fMRI brain images and EEG brain waves extracted from human multi-perception mechanism involved in a particular task are peculiar ones with respect to the specific state or the relatedpart of a stimulus. Based on this point of view, we propose a way of peculiarity oriented mining for multi-aspect analysis in multiple human brain data, without using conventional image processing to fMRI brain images and frequency analysis to brain waves. The proposed approach provides a new wayfor automatic analysis and understanding of human brain data to replace human-expert centric visualization. We attempt to change the perspective of cognitive scientists from a single type of experimental data analysis towards a holistic view.
面向特性的多面向脑数据分析研究人类多感知机制
为了研究人类高级大脑活动的结构,需要各种各样的大脑分析方法。人们观察到,从人类参与特定任务的多感知机制中提取的fMRI脑图像和EEG脑电波等多脑数据是相对于刺激的特定状态或相关部分的特殊数据。基于这一观点,我们提出了一种面向特性的挖掘方法,用于对多个人脑数据进行多方面分析,而无需对fMRI脑图像进行传统的图像处理,对脑电波进行频率分析。该方法为人脑数据的自动分析和理解提供了一种新的方法,以取代以人类专家为中心的可视化。我们试图改变认知科学家的观点,从单一类型的实验数据分析转向整体观点。
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