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Brain research has become a hot topic in the recent years. Electroencephalography (EEG) is a powerful technique for studying the electrophysiological dynamics of human brain.

MSc Deqing Wang’s research investigates an advanced signal processing method for analysing multiway EEG data, which is called tensor decomposition. Using tensor decomposition, multi-domain features, such as space, frequency and time, can be extracted representing brain activities.

In Wang’s research, experiments were carried out on real-world EEG data collected by external stimuli, such as naturalistic continuous music stimulus and repeated proprioceptive stimulus.

The proposed tensor decomposition algorithms are able to efficiently extract meaningful EEG features that are related to cognitive processes.

The proposed methods can be used to analyse a variety of multiway data in brain research and cognitive neuroscience.

MSc Deqing Wang defends his doctoral dissertation in Mathematical Information Technology "Extracting meaningful EEG Features using constrained tensor decomposition" at the Ä¢¹½Ö±²¥ on Thursday December 12th, 2019. The event takes place in Agora building, hall Alfa (Mattilanniemi 2, 40100 Jyväskylä). The Opponent is Professor Pauli Miettinen (University of Eastern Finland) and Custos Professor Tapani Ristaniemi (Ä¢¹½Ö±²¥). The doctoral dissertation is held in English.

Further information:

Deqing Wang, deqing.wang@foxmail.com, +358 40 377 8570

Deqing Wang (b. 1986, Chaoyang City, Liaoning Province, China) received his B.E. degree in automation and his M.E. degree in pattern recognition and intelligent system from Harbin Engineering University, Harbin, China, in 2009 and 2012, respectively.

The dissertation is published in the series of JYU Dissertations, number 169, 61 p., Jyväskylä 2019, ISSN: 2489-9003, ISBN: 978-951-39-7968-3 (PDF). Link: