Multimodal Neuroimgaing, EEG, fNIRS, Stroke, Dry electrodes, Diseases Prediction, Wearables
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 (Westlake University) 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.
She is currently a Research Assistant Professor at CenBRAIN (Cutting-Edge Net of Biomedical Research And INnovation )Center in the School of Engineering of Westlake University. She works on the EEG-fNIRS system and other wearable devices for stroke prediction.
Yun-Hsuan Chen received her Bachelor's degree in the Department of Materials Science and Engineering of National Tsing Hua University (Hsinchu, Taiwan). Then she got an Erasmus Mundus scholarship to pursue her joint master degree in molecular nano- and bio-photonics for telecommunications and biotechnologies (Monabiphot) program in ENS Cachan (Paris, France), Complutense University of Madrid (Madrid, Spain), and Delft University of Technology (Delft, the Netherlands). Later she joined IMEC (Leuven, Belgium) and received her Ph.D. in Electrical Engineering of University of Leuven (Leuven, Belgium).
Representative publications:
[1] Y.-H. Chen and M. Sawan, "Trends and Challenges of Wearable Multimodal Technologies for Stroke Risk Prediction," Sensors, vol. 21, no. 2, 2021, Art no. 460, doi: 10.3390/s21020460.
[2] Y. H. Chen et al., "Soft, Comfortable Polymer Dry Electrodes for High Quality ECG and EEG Recording," (in English), Sensors, Article vol. 14, no. 12, pp. 23758-23780, 2014, doi: 10.3390/s141223758.
[3] M. Sawan, J. Yang, M. Tarkhan, J. Chen, M. Wang, C. Wang, F. Xia, and Y.-H. Chen, “Emerging Trends of Biomedical Circuits and Systems,” Foundations and Trends® in Integrated Circuits and Systems, vol. 1, no. 4, pp. 217-411, 2021.
[4] Y. H. Chen, M. Op de Beeck, L. Vanderheyden, V. Mihajlovic, B. Grundlehner, and C. Van Hoof, "Comb-shaped polymer-based Dry electrodes for EEG/ECG measurements with high user comfort," in Engineering in Medicine and Biology Society (EMBC), 35th Annual International Conference of the IEEE, 3-7 July 2013, pp. 551-554, doi: 10.1109/EMBC.2013.6609559.
[5] X. Yang, Y.-H. Chen, F. Xia, and M. Sawan, "Photoacoustic imaging for monitoring of stroke diseases: A review," Photoacoustics, vol. 23, p. 100287, 2021, doi: https://doi.org/10.1016/j.pacs.2021.100287.
[6] T. Torfs, Y.-H. Chen, H. Kim, and R. F. Yazicioglu, "Noncontact ECG recording system with real time capacitance measurement for motion artifact reduction," IEEE transactions on biomedical circuits and systems, vol. 8, no. 5, pp. 617-625, 2014.
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