题目：Dynamic Brain Mapping and Brain-Computer Interface
Bin He is Department Head and Trustee Professor of Biomedical Engineering
at Carnegie Mellon University, Pittsburgh, USA, and a Professor at the Carnegie Mellon Neuroscience Institute. Dr. He has made significant research contributions to the fields of neuroengineering and biomedical imaging, including electrophysiological source imaging, noninvasive brain-computer interface, and neuromodulation. Dr. He has received a number of awards including the IEEE Biomedical Engineering Award, the Academic Career Achievement Award from the IEEE Engineering in Medicine and Biology Society, the Established Investigator Award from the American Heart Association, among others. He is an elected Fellow of International Academy of Medical and Biological Engineering, IEEE, American Institute of Medical and Biological Engineering, and Biomedical Engineering Society. Dr. He served as a past President of the IEEE Engineering in Medicine and Biology Society, the International Society for Bioelectromagnetism, and the International Society for Functional Source Imaging. Dr. He served as the Editor-in-Chief of IEEE Transactions on Biomedical Engineering from 2013-2018, and is the Chair of the International Academy of Medical and Biological Engineering.
Brain activity is distributed over the 3-dimensional volume and evolves in time. Mapping spatio-temporal distribution of brain activation with high spatial resolution and high temporal resolution is of great importance for understanding the brain and aiding in the clinical diagnosis and management of brain disorders. Electrophysiological source imaging (ESI) from noninvasively recorded high density electroencephalogram (EEG) has played a significant role in advancing our ability to image brain function and dysfunction. We will discuss principles and current state of EEG-based ESI in localizing and imaging human brain activity with applications to seizure localization. Promising clinical results validated by intracranial recordings and surgical resection outcomes demonstrate the merits of noninvasive EEG-based ESI in mapping epileptogenic zones, aiding surgical treatment of intractable epilepsy. We will also discuss our recent progress in EEG based brain-computer interface, for controlling of a drone and robotic arm from noninvasive EEG signals using a motor imagery paradigm. Finally, we will briefly overview the other neuroeingeering activities at Carnegie Mellon University.