Decoding Cognitive States from Behavior and Physiology Using a Bayesian Approach: Present and Future
September 6, 2019
Fuller Labs 320
Cognitive processes, such as learning and cognitive flexibility, are both difficult to measure and to sample continuously using objective tools because cognitive processes arise from distributed, high-dimensional neural activity. For both research and clinical applications, that dimensionality must be reduced. To reduce dimensionality and measure underlying cognitive processes, I proposed a modeling framework in which a cognitive process is defined as a low-dimensional dynamical latent variable—called a cognitive state, which links high-dimensional neural recordings and multidimensional behavioral readouts. This framework allows us to decompose the hard problem of modeling the relationship between neural and behavioral data into separable encoding-decoding approaches. I first use a state-space modeling framework, the behavioral decoder, to articulate the relationship between an objective behavioral readout (e.g., response times) and cognitive state. The second step, the neural encoder, involves using a generalized linear model (GLM) to identify the relationship between the cognitive state and neural signals, such as local field potential (LFP). I then use the neural encoder model and a Bayesian filter to estimate cognitive state using neural data (LFP power) to generate the neural decoder. We applied this framework to estimate an underlying cognitive state from neural data in human participants (N=8) performing a cognitive conflict task. We successfully estimated the cognitive state within the 95% confidence intervals of that estimated using behavior readout for an average of 90% of task trials across participants. In contrast to previous encoder-decoder models, the proposed modeling framework incorporates LFP spectral power to encode and decode a cognitive state. The framework allows us to capture the temporal evolution of the underlying cognitive processes, which could be key to the development of closed-loop experiments and treatments.
Ali Yousefi is an assistant professor at the Department of Computer Science, WPI. Before joining WPI, he was a research scientist at Boston University in the department of mathematics & statistics, as well as an instructor with Harvard Medical School in the department of psychiatry. He earned his Ph.D. with a major in electrical engineering and a minor in biomedical engineering from the University of Southern California. He has done postdoctoral training at both Harvard Medical School and the Mayo Clinic.
With postdoctoral training in engineering, computational neuroscience and statistics; his research art WPI will focus on developing methodological solutions to problems concerning neuroscience data analysis and Brain Computer Interface (BCI) applications.
Light Refreshments will be served.