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Classification of Relative Object Size from Parietooccipital Hemodynamics Using Type-2 Fuzzy Sets

机译:使用2型模糊集从顶枕血液动力学分类相对对象大小

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During the past two decade researchers have been exploring the mechanism of object shape and depth perception using EEG and fMRI. However, the underlying cortical process of perceiving different object sizes from a constant visual distance has never been explored. This paper provides a novel understanding of relative object size classification based on direct measure of parieto-occipital hemodynamics using functional near infrared spectroscopy (fNIRS). The cortical response is recorded from subjects engaged in visual perception task of relative object size. The signal is preprocessed (artifact removal) for construction of 176 features, which are thus reduced to 22 features using particle swarm optimization (PSO) technique. The reduced features are subsequently fed into an interval type -2 fuzzy set to classify the perceived objects (based on the underlying hemodynamic data) into three different classes: LARGE, MEDIUM and SMALL. Experimental analysis shows that the proposed feature-selection and classification framework attain higher classification accuracy which reaches over 87% in the classification of large objects. Analysis, further undertaken to know the underlying neurovascular mechanisms, reveals a distinct dorso-ventral shift (shall-medium-to-large) in parieto-occipital hemodynamic load which can be observed from the topographic brain activation. The average activation shifts are measured as 73.35 degrees in the right hemisphere compared to 93.71 degrees in the left hemisphere. The experimental outcomes could provide a novel measure in cortical hemodynamic features based perception of object size. In future, it could provide justification towards the visually challenged persons with perceptual difficulties.
机译:在过去的十年中,研究人员一直在探索使用EEG和fMRI进行物体形状和深度感知的机制。然而,从未探索过从恒定的视觉距离感知不同物体大小的潜在皮层过程。本文基于使用功能近红外光谱(fNIRS)直接测量顶枕血液动力学的相对对象大小分类,提供了一种新颖的理解。皮质反应是从从事相对物体大小的视觉感知任务的受试者中记录的。信号经过预处理(去除伪像)以构建176个特征,因此使用粒子群优化(PSO)技术将其减少为22个特征。缩减后的特征随后被输入到间隔类型-2模糊集中,以将感知到的对象(基于潜在的血液动力学数据)分类为三个不同的类别:大,中和小。实验分析表明,提出的特征选择和分类框架具有较高的分类精度,在大对象分类中达到了87%以上。进一步进行分析以了解潜在的神经血管机制,发现顶枕顶血流动力学负荷有明显的背腹向腹侧移位(应从中到大),这可以从地形大脑的激活情况中观察到。右半球的平均激活位移为73.35度,而左半球的平均激活位移为93.71度。实验结果可为基于物体大小感知的皮质血流动力学特征提供一种新颖的测量方法。将来,它可以为有视觉障碍的视力障碍人士提供辩护。

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