Please join us as Dr. Akaysha Tang, Associate Professor of Psychology at the University of New Mexico and former Program Director at the National Science Foundation, shares her research on cognitive neuroscience on Oct. 31 at 10:00 a.m. in the Experimental Sciences Building, room 120.
“Reliability and Interpretability of High-Density EEG-Based Source Imaging—Enabling Tools for a Cognitive Neuroscience of the Individual”
A great deal can be learned from patterns of brain activity if one can obtain measures of such activity reliably across time and across individual and if the measures are interpretable in relation to the behavioral and mental states and in terms of known neural mechanisms. In this talk, I will present empirical evidence to demonstrate novel capabilities attainable by applying Second Order Blind Identification (SOBI, Belouchrani & Cardoso, 1993, 1997) to individual (as opposed to group) high density EEG data. I will show that (1) SOBI can provide description of brain activity in terms of signals from specific functional brain regions, instead of mixtures of signals measured at the electrodes locations on the scalp (reduced ambiguity in signal interpretation and increased S/N and reliability); (2) SOBI can arrive at such a description simultaneously for multiple brain regions as well as noisy sources, such as ocular artifacts (no need to throw away large quantity of data); (3) SOBI can achieve such a description without requiring the participant to maintain fixation and eliminate eye movement (source separation under the condition of free eye movement); (4) SOBI can achieve such a description without requiring the participant to engage in a task (source separation from EEG obtained during sleep or coma); (5) SOBI eliminate the need for several major subjective decisions in conventional source localization (increase reproducibility in data analysis); (6) SOBI is deterministic and efficient (increase feasibility in usage); (7) SOBI does not require the use of group data (single-subject and single-trial analysis). Together, these new capabilities may open the door to a new kind of cognitive neuroscience, where it is possible to measure brain activity patterns of an individual and predict behavioral outcomes of an individual.
