A 22-page-long paper written by Professor Chen Xun from School of Instrument Science and Opto-electronics Engineering upon invitation entitled “Joint Blind Source Separation for Neurophysiological Data Analysis: Multiset and Multimodal Methods” was published on IEEE Signal Processing Magazine in April.
IEEE Signal Processing Magazine is an authoritative journal in signal processing. The magazine invites accomplished scholars regularly to write reviews in summary of the significance and status of the related studies.
Professor Chen has engaged in the study of bio-medical signal and image processing for a long time. Based on the actual demands for medical signal processing and data analysis, he put forward multiset and multimodal blind source separation (BSS) methods with ingenuity. The methods he proposed have been successfully applied in the multimodal data analysis of Parkinson's disease.
Upon the invitation of Professor Min Wu, the editor in chief of IEEE Signal Processing Magazine, has been composing a feature article that elaborates on the joint blind source separation (JBSS) technology. JBSS enjoys a vast prospect in neurophysiological applications and image data fusion analysis, which would be instrumental in clinical studies and China Brain Project.
Professor Chen is among the few authors who publish papers upon invitation. The study has been funded by the Young Scientist Fund and the General Program of National Natural Science Foundation of China.
Access to the paper: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7461016
Written by & Photo Credit: Cheng Juan
Edited by: Wang Jian