TCCI Investigator Yang Zhi Publishes Paper on a Technology Dynamically Tracking State Anxiety via Multi-modal Data and Machine Learning
Research News
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May 11, 2022
Professor Yang Zhi, Tianqiao and Chrissy Chen Investigator and a member of Jiao Tong University School of Medicine affiliated Shanghai Mental Health Center Shanghai and his team recently published a paper in Frontiers in Psychiatry, which introduces a tracking model of state anxiety with high temporal resolution. To capture the dynamic changes of state anxiety levels (measure STAI-S scores before and after the tests), researchers induced the participants’ state anxiety through exposure to aversive pictures or the risk of electric shocks and simultaneously-recorded multi-modal data, including dimensional emotion ratings, electrocardiogram, and galvanic skin response. They also trained and validated machine learning models to predict state anxiety based on psychological and physiological features extracted from the multi-modal data.
Figure 1. Prediction of STAI-S using multi-modal data.
The present study aims to build a dynamic tracking model of state anxiety based on multi-modal data extracted from psychological and physiological features, which reflects the dynamic changes of individual state anxiety under high temporal resolution. This quantitative model provides fine-grained and sensitive measures of state anxiety levels for future affective brain-computer interaction and anxiety modulation studies.
Associate researcher Ding Yue and postgraduate student Liu Jingjing from Shanghai Jiao Tong University School of Medicine affiliated Shanghai Mental Health Center affiliated are the lead authors of the paper. This research was funded by The National Natural Science Foundation of China, Science and Technology Commission of Shanghai Municipality, and the Tianqiao and Chrissy Chen Institute.
Professor Yang Zhi, Tianqiao and Chrissy Chen Investigator and a member of Jiao Tong University School of Medicine affiliated Shanghai Mental Health Center Shanghai and his team recently published a paper in Frontiers in Psychiatry, which introduces a tracking model of state anxiety with high temporal resolution. To capture the dynamic changes of state anxiety levels (measure STAI-S scores before and after the tests), researchers induced the participants’ state anxiety through exposure to aversive pictures or the risk of electric shocks and simultaneously-recorded multi-modal data, including dimensional emotion ratings, electrocardiogram, and galvanic skin response. They also trained and validated machine learning models to predict state anxiety based on psychological and physiological features extracted from the multi-modal data.
Figure 1. Prediction of STAI-S using multi-modal data.
The present study aims to build a dynamic tracking model of state anxiety based on multi-modal data extracted from psychological and physiological features, which reflects the dynamic changes of individual state anxiety under high temporal resolution. This quantitative model provides fine-grained and sensitive measures of state anxiety levels for future affective brain-computer interaction and anxiety modulation studies.
Associate researcher Ding Yue and postgraduate student Liu Jingjing from Shanghai Jiao Tong University School of Medicine affiliated Shanghai Mental Health Center affiliated are the lead authors of the paper. This research was funded by The National Natural Science Foundation of China, Science and Technology Commission of Shanghai Municipality, and the Tianqiao and Chrissy Chen Institute.
