BME Seminar Series: Fusing Simultaneously Acquired EEG and fMRI to Infer Spatiotemporal Dynamics of Cognition in the Human Brain
Paul Sajda, Ph.D., Professor of Biomedical Engineering, Electrical Engineering and Radiology, Columbia University
Advances in neural signal and image acquisition as well as in multivariate signal processing and machine learning are enabling a richer and more rigorous understanding of the neural basis of human decision-making. Decision-making is essentially characterized behaviorally by the variability of the decision across individual trials—e.g., error and response time distributions. To infer the neural processes that govern decision-making requires identifying neural correlates of such trial-to-trial behavioral variability. In this talk I will discuss how were are using simultaneously acquired electroencephalograpy (EEG) and functional magnetic resonance imaging (fMRI) to infer the spatiotemporal dynamics that underlie the formation and execution of rapid perceptual decisions. Focus will be on the different approaches we have developed to couple the trial-to-trial variability in the EEG and hemodynamic signals, and how to relate the resulting measures to elements of the perceptual decision-making process.
Dr. Paul Sajda is a Professor of Biomedical Engineering, Electrical Engineering and Radiology (Physics) at Columbia University. He is also a Member of Columbia’s Data Science Institute. He received a BS in electrical engineering from MIT in 1989 and an MSE and PhD in bioengineering from the University of Pennsylvania, in 1992 and 1994, respectively. Professor Sajda is interested in what happens in our brains when we make a rapid decision and, conversely, what processes and representations in our brains drive our underlying preferences and choices, particularly when we are under time pressure. His work in understanding the basic principles of rapid decision-making in the human brain relies on measuring human subject behavior simultaneously with cognitive and physiological state. Important in his approach is his use of machine learning and data analytics to fuse these measurements for predicting behavior and infer brain responses to stimuli. Professor Sajda applies the basic principles he uncovers to construct real-time brain-computer interfaces that are aimed at improving interactions between humans and machines. He is also applying his methodology to understand how deficits in rapid decision-making may underlie and be diagnostic of many types of psychiatric diseases and mental illnesses. Professor Sajda is a co-founder of several neurotechnology companies and works closely with a range of scientists and engineers, including neuroscientists, psychologists, computer scientists, and clinicians. He is a fellow of the IEEE, AMBIE and AAAS and is also Editor-in-Chief of IEEE Transactions on Neural Systems and Rehabilitation and Chair of the IEEE Brain Initiative.