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Academic Report: Real-world Neuroimaging: an Intersection of Neuroscience, Neurotechnologies, and Machine Learning



Lecture: Real-world Neuroimaging: An Intersection of Neuroscience, Neurotechnologies, and Machine Learning  

Speaker: Prof. Zhong Ziping  

Venue:  Room 401, Building of School of Information Science and Engineering, East Campus  

Date:   Monday, October 29, 2018  

Time:  9:00  

Speaker Introduction

Zhong Ziping, American Chinese, is a Professor of the University of California, San Diego (UCSD), Deputy Director of the Swartz Computational Neuroscience Center, Deputy Director of the UCSD Advanced Neuroengineering Center, and an adjunct professor at the National Chiao Tung University in Taiwan, IEEE Fellow. His research interests involve computational neuroscience, brain-computer interface, and machine learning. It has established a revolutionary technology that uses blind source separation to decompose multi-channel EEG/MEG/ERP and fMRI data. Many research results have been published in many international top journals such as SCIENCE, PNAS, PROCEEDINGS OF THE IEEE, and the total number of papers cited by GOOGLE. Up to 22,700 times, the H factor is as high as 59.

Lecture Content

The past twenty years have witnessed remarkable advances in neuroscience and neurotechnologies. However, nearly all the neuroscience research studies were conducted in well-controlled laboratory settings. It has been argued that fundamental differences between laboratory-based and naturalistic human behavior may exist. It remains unclear how well the current knowledge of human brain function translates into the highly dynamic real world (McDowell, 2014). Therefore, there is a need to study the brain in ecologically valid environments to truly understand how the human brain functions to optimally control behavior in face of ever-changing physical and cognitive circumstances. To this end, we have developed and validated transformative techniques and tools to collect laboratory-grade neural, physiological, and behavioral data from unconstrained, freely moving subjects in everyday environments. We have also developed and applied state-of-the-art machine-learning algorithms to find statistical relationships among the variations in environmental, behavioral, and functional brain dynamics. This talk will focus on the development of tools for real-world neuroimaging research and the results of sample neuro cognitive studies.

This report is suitable for teachers and students in computer, electronics, control, biomedical engineering and other majors interested in computing neuroscience and information processing.

All are welcome.

School of Information Science and Engineering

[Translated by Liu Shuai]

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