Ph.D. Candidate

Neuroscience Program

Education:

B.S. in Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania, USA, 2019

Biography:

I majored in Neuroscience and minored in Bioengineering and Chemistry during my undergraduate at the University of Pittsburgh. I worked in Dr. Citlali López-Ortiaz’s Neuroscience of Dance in Health and Disability lab during my first year in Neuroscience Program, and then joined HDCL in May 2020. My first project involved the evaluation of an arm rigidity training simulator that mimics different severity levels of lead pipe rigidity to promote the training experience for medical professionals.

My current project, RADWear, focuses on the classification of stress and anxiety utilizing multimodal data from wearable devices, Hexoskin smart shirt, and Empatica E4 wristband. Particularly, we are targeting 3rd – 4th medical students who are undergoing rotations and are often facing significant stress and anxiety. In this project, we invite medical students to wear those 2 devices during their rotation days to collect their physiological signals and self-report their emotional states by the end of every day.

WEAR project, an expansion project from RADWEAR, is another focus that I aim to use a combination of research-grade and wearable sensors in both lab-controlled and ‘in-the-wild’ situations (i.e., participants remotely collect data during their daily routine using provided wearables) to examine the correlation between brain activity and peripheral physiological signals during stressful events among college students. To comprehensively capture the stress responses in lab-controlled conditions, We utilize a battery of sensor modalities such as electroencephalogram (EEG), electromyography (EMG), research-grade electrocardiogram (ECG), and Galvanic skin response (GSR) sensor, in conjunction with Hexoskin smart shirt and Empatica E4 wristband. We are focusing on Carle medical students and UIUC students in STEM fields with an emphasis on women and underrepresented minority groups. The data collection protocol consists of 2 in-lab sessions and a 10-day remote data collection to monitor the changes in stress levels during the academic semesters.

As future directions and for my Ph.D. dissertation, I will be focusing on 1) exploring patterns of physiological activities (particularly EEG and ECG) in response to different types of stressors, 2) examining the interplay between brain and cardiac activities, and 3) implementing machine learning algorithms that utilize multimodal sensor signals for both general and personalized stress/anxiety detection.

Research Interests:

• Computational Neuroscience
• Neurophysiology of stress and anxiety
• Signal processing
• Machine learning

Personal Interests:

• Reading
• Building miniature houses
• Playing video games and Lego
• Collecting pressed penny
• Bird watching
• Gardening

Feel free to check my personal website and Curriculum Vitae!

LinkedIn| Google Scholar

Email: maoyuan2(AT)illinois(DOT)edu