Using Machine Learning to Better Understand the Human Brain and Behavior

Abstract

Artificial intelligence (AI) including machine learning (ML) has made considerable contributions to our ability to attack problems with complex, unstructured and unlabeled data. While the human brain has long regarded as a source of inspiration for AI, a contentious question is whether AI can advance the understanding of the human brain and behavior. My presentation will focus on (1) how DL/ML can help us to explore human brain dynamics and emotional responses; (2) how ML finds the associations between multimedia contents and the human brain; and (3) how transfer learning can improve the performance of brain-computer interfaces. Study results show that ML/DL may open a novel and revolutionary window into complex neural and physiological data, leading to a more detailed understanding of the strengths and limitations of the human mind, plus possible applications to medicine and cognitive testing/monitoring.