In this research we seek to develop a means of detecting cognitive states relevant to addiction research in real-time, using a combination of fMRI and EEG data. Our principal tools are extensions of methods used for automated pattern analysis and machine learning, which we believe are better suited to the problem because of their inherent ability to incorporate features that are extensive throughout the brain and are therefore reflective of integrated processing across functional regions. Our goal ultimately is to have a tool that can be used in the context of neurofeedback, allowing human subject or patient to receive an indication of activity in their brains associated with these states and to enable them to learn to control these cognitive/affective states by controlling the brain activity.
Originally created: 18 Jun 2010 Current author: Pauline Jaturongp...
Jane & Terry Semel Institute for Neuroscience & Human Behavior
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UCLA Health System School of Medicine
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