舒华老师课题组邀请了美国国家科学基金会的Akaysha Tang教授到实验室做学术报告，欢迎感兴趣的老师和同学参加。以下为报告信息：
时间：2015年7月7日，10:30AM
地点：英东楼422会议室
报告题目：Reliability, Interpretability, and Robustness of High-Density EEG-Based Source Imaging—Enabling Tools for an individualized science of learning
报告人：Akaysha Tang
China Program Director
East Asia & Pacific Region
Office of Internal Science & Engineering
National Science Foundation
Associate Professor (on leave)
Department of Psychology
Department of Neurosciences
University of New Mexico
http://atlab.unm.edu
报告摘要:This talk is aimed at stimulating novel research designs for those who are interested in functional brain mapping with millisecond temporal resolution. Electroencephalography (EEG) is a relatively inexpensive and potentially field-friendly tool for studying neural processing that requires millisecond resolution. However due to its widely perceived limitation in spatial resolution, EEG has not been used as a tool of choice in mapping brain structures to functions. In this talk, I will present empirical evidence to demonstrate that novel capabilities of structure-function mapping with millisecond resolution may be achieved via Second Order Blind Identification (SOBI, Belouchrani & Cardoso, 1993, 1997), a blind source separation (BSS) algorithm. I will show that using SOBI, one can (1) obtain 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) 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) achieve such a description without requiring the participant to maintain fixation and eliminate eye movement (source separation under the condition of continuing free eye movement); (4) achieve such a description without requiring the participant to engage in a task (source separation from EEG obtained during sleep or coma); (5) eliminate the need for several major subjective decisions in conventional source localization (increase reproducibility in data analysis); (6) increase feasibility in real time source tracking; (7) achieve single-subject and single-trial analysis of ERP and on-going activity. The audience is encouraged to bring cases of potential studies that may benefit from these new capabilities. No knowledge of mathematics is required for understanding this talk.
