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Decoding color of stimuli given to a human subject from functional magnetic resonance imaging voxel patterns using machine learning algorithm

机译:使用机器学习算法从功能磁共振成像体素模式解码对人类对象的刺激颜色

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A brain decoding of visual stimuli using various machine learning is proposed in order to make a foundation of brain computer interface. Visual stimuli that are representations of objects, shapes, colors, and so on, are important information for human perception. Some of properties of processing of visual information in human brain are revealed, for example existence of neuron responding an orientation of line segment. This research reveals the precision of pattern recognition using supervised machine learning of human brain activity when human see color circle drawn In a display. Support vector machine with various kernel, neural network, random forest, and sparse logistic regression are employed in this research and compared among each other. The result shows that the highest precision Is 71% for predicting color of circle from three colors using sparse logistic regression.
机译:为了建立大脑计算机接口的基础,提出了使用各种机器学习对视觉刺激进行大脑解码的方法。视觉刺激是对象,形状,颜色等的表示形式,是人类感知的重要信息。揭示了人脑中视觉信息处理的一些特性,例如,存在响应线段方向的神经元。这项研究揭示了当人们看到显示器上的彩色圆圈时,使用有监督的机器学习对人脑活动进行模式识别的精度。本研究采用具有各种核,神经网络,随机森林和稀疏逻辑回归的支持向量机,并进行了相互比较。结果表明,使用稀疏逻辑回归从三种颜色预测圆形的颜色的最高精度为71%。

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