You are invited to join the upcoming iHealthtech - Department of Psychological Medicine Joint Webinar on 8 February 2023, Wednesday, 8 am - 9 am on Zoom.
𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐟𝐍𝐈𝐑𝐒-𝐛𝐚𝐬𝐞𝐝 𝐁𝐫𝐚𝐢𝐧-𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐈𝐧𝐭𝐞𝐫𝐟𝐚𝐜𝐞 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡
Functional near-infrared spectroscopy (fNIRS) is a neuroimaging tool that has great potential to provide an objective and scientific assessment of mental health. In our recent study using the technique, we employed machine learning-based mental state classification to increase the efficiency and accuracy of the evaluation. The study has addressed the shortcomings of many mental stress detection studies that focused on discriminating the mental state with and without the experimental stressor. Therefore, we incorporated baseline stress in our machine learning model to successfully classify baseline stress (high/low) and the mental state (task/rest) with the support vector machine classifiers.
In another study using the fNIRS, we evaluated the neurocognitive mechanism underlying the cognitive impairments caused by depression by observing the functional connectivity during a cognitive task. We found a reduction in prefrontal activation and weaker overall interhemispheric subregion-wise correlations in the patient group compared with corresponding values in the control group. These studies have shown fNIRS's potential to be an efficient and accurate neuroimaging tool for clinical research and to function as a brain-computer interface for the general public.
Suh-Yeon Dong received her B.S., M.S., and PhD degrees from the School of Electrical Engineering at Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea. She worked as a researcher at the Brain Science Research Center, Daejeon, South Korea, from August 2011 to January 2012 and as a BK21+ postdoctoral researcher at the Korea Institute of Science and Technology (KAIST) from 2016 – 2018. She is currently an Assistant Professor at the Department of Information Technology Engineering at Sookmyung Women's University. Her research interests are human-computer interaction, machine learning, bio/brain signal processing, cognitive neuroscience, and mobile healthcare application.
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