基于纹理滤波器和塔克分解的ERP检测器

Rubén Álvarez-González, Andres Mendez-Vazquez
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引用次数: 1

摘要

视觉是人类获取外部信息的主要感官通道。了解人类大脑对视觉刺激的反应将有助于我们开发更好的脑机接口,并描述人类大脑的活动反应。追踪大脑活动的一种技术是功能性磁共振成像(fMRI),它使用依赖血氧水平的成像或bold对比成像来显示刺激之前、期间和之后大脑中的血氧情况。在不同的研究中心,识别由特定刺激引起的大脑活动是一个课题。当流行的分类器在实际应用中不能提供完美的准确性时,它们失败的可能原因可能是算法的缺陷和数据的内在困难。在机器学习和深度学习中,模型大多仍然是黑盒子;卷积神经网络(CNN)也不例外。这种对机器学习管道设计和特征提取过程的理解将提供对分类模型的深入了解。
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ERP Detector using Texture Filters and Tucker Decomposition
Vision is the dominant sensory channel by which humans acquire external information. Understanding how the human brain responds to a visual stimulus will help us develop better brain-machine interfaces and describe the human-brain activity response. One technique for tracking brain activity is functional magnetic resonance imaging (fMRI) using blood-oxygen-level-dependent imaging or BOLD-contrast imaging to show the blood oxygenation in the brain before, during and after a stimulus. Identifying the brain activity provoked by a given stimulus is a topic in different research centers.When popular classifiers do not provide perfect accuracy in a practical application, possible causes of their failure can be deficiencies in the algorithms and intrinsic difficulties in the data. In machine and deep learning, models mostly remain black boxes; convolutional neural networks (CNN) are no exception. This understanding of the design of the machine-learning pipeline and the feature-extraction process will provide insight into what a classification model could be.
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