報告時(shí)間：2018年10月29日（周一）9:00

報告地點(diǎn)：東校區信息館401

報告題目：《Real-worldNeuroimaging: an Intersection of Neuroscience, Neurotechnologies, and MachineLearning》

報告人：鐘子平

報告人簡(jiǎn)介：鐘子平，美籍華人，加州大學(xué)圣地亞哥分校（UCSD）教授，Swartz計算神經(jīng)科學(xué)中心副主任，UCSD先進(jìn)神經(jīng)工程中心副主任，臺灣國立交通大學(xué)等高校兼職教授，IEEE Fellow。研究方向主攻計算神經(jīng)科學(xué)、腦機接口、機器學(xué)習。其建立了應用盲源分離以分解多通道EEG/MEG/ERP和fMRI數據的變革技術(shù)，諸多研究成果曾在SCIENCE、PNAS、PROCEEDINGS OF THE IEEE等多家國際頂級期刊發(fā)表，發(fā)表論文GOOGLE引用次數總和達22700次，H因子高達59。

報告內容簡(jiǎn)介：The past twenty years havewitnessed remarkable advances in neuroscience and neurotechnologies. However,nearly all the neuroscience research studies were conducted in well-controlledlaboratory settings. It has been argued that fundamental differences betweenlaboratory-based and naturalistic human behavior may exist. It remains unclearhow well the current knowledge of human brain function translates into thehighly dynamic real world (McDowell, 2014). Therefore, there is a need to studythe brain in ecologically valid environments to truly understand how the humanbrain functions to optimally control behavior in face of ever-changing physicaland cognitive circumstances.To this end, we have developed and validated transformative techniquesand tools to collect laboratory-grade neural, physiological, and behavioraldatafrom unconstrained, freely movingsubjects in everyday environments. We have alsodeveloped and applied state-of-the-artmachine-learning algorithms to find statistical relationships among thevariations in environmental, behavioral, and functional brain dynamics.Thistalk will focus on the development of tools for real-world neuroimagingresearch and the results of sample neurocognitive studies.

本報告適合于對計算神經(jīng)科學(xué)與信息處理感興趣的計算機、電子、控制、生物醫學(xué)工程等專(zhuān)業(yè)的教師和學(xué)生，歡迎廣大師生參加！

信息科學(xué)與工程學(xué)院

2018年10月26日