Postdoctoral Fellow, started Mar. 2019
Ph.D. University of Leuven
Dr. Yun-Hsuan CHEN got her bachelor’s degree in Department of Materials Science and Engineering of National Tsing Hua University (Taiwan). Her Erasmus Mundus joint master’s degree in molecular nano- and bio-photonics for telecommunications and biotechnologies (Monabiphot) program is offered by ENS Cachan (France), Complutense University of Madrid (Spain) and Delft University of Technology (the Netherlands). Then she joined Body Area Networks group in imec as a PhD researcher. In 2016, she received her Ph.D. in Electrical Engineering from University of Leuven, Belgium. Her doctoral thesis entitled 'Polymer-based dry electrodes for biopotential measurements' was supervised by Prof. Chris Van Hoof at imec.
Dr. Yun-Hsuan Chen works more than five years on developing and characterizing various types of electrodes (various designs and materials) for wearable devices. In addition, she has experience on validating the developed electrodes electrically using potentiostat and analyzing the recorded ECG signals on human subjects and EEG signals on patients with epilepsy. One of her research projects in CenBRAIN is brain diseases detection and prediction using clinical signals recorded by a hybrid EEG-fNIRS system and other wearable devices. Multimodal EEG-fNIRS neuroimaging technique recording neuron electrical signals and hemodynamic responses simultaneously support investigating the relationship between these two types of physiological parameters. Compared with CT and MRI, EEG-fNIRS system being non-invasive, a rather low cost, easy to set up and operate technology is perfect for real-time brain activity monitoring in natural settings/ at bedside. Now, the project focuses on the application of multimodal EEG-fNIRS system on stroke patients. Another research project she works on is impedance measurement of interdigitated electrodes (IDEs) for biosensors. The impedance of IDEs varies when the interface of electrode/electrolyte changes. The variation can be measured using a potentiostat. The equivalent circuit of the impedance measurement can be applied to optimize the specification of IDEs and the reactants.
 Y.-H. Chen et al., "Trends and Challenges of Wearable Multimodal Technologies for Stroke Risk Prediction", MDPI Sensors, Vol. 21, No. 2, Jan 2021, pp. 460.
 Y. H. Chen et al., "Soft, Comfortable Polymer Dry Electrodes for High Quality ECG and EEG Recording," Sensors, Article vol. 14, no. 12, pp. 23758-23780, Dec 2014, doi: 10.3390/s141223758.
 Y.-H. Chen et al., "Polymer-based dry electrodes for high user comfort ECG/EEG measurements," in 8th International Conference & Exhibition on Integration Issues of Miniaturized Systems-MEMS, NEMS, ICs and Electronic Components, 2014: Apprimus Verlag, pp. 329-336.