ERP Detector using Texture Filters and Tucker Decomposition

Abstract:

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.

Año de publicación:

2020

Keywords:

• FMRI
• Machine learning
• Computer Vision
• tucker decomposition

Fuente:

scopus