VR & R Buffet Food Selection

Neurophysiological Variations in Food Decision-Making within Virtual and Real Environments (BHI 2019)

ROLE: Research Project Lead (SAIL Lab)

SKILLS UTILIZED: Sensor Data Collection, Data Processing, Behavioral Coding, Data Analysis, Computational Modeling of Emotion-Driven Behavior 


SOFTWARE: Unreal Engine 4, Artinis OxySoft, Pupil Capture and Player, Consensys Pro, Microsoft Excel

HARDWARE: HTC Vive Pro, Artinis OCTAMON fNIRS Headset, Pupil Labs Pupil EyeTracker, Shimmer Consensys Sensor Kit



Simple lifestyle changes such as eating healthy foods and getting enough exercise could significantly reduce the risk for obesity-related diseases such as diabetes, heart disease, stroke, and cancer. However, changing eating behavior is challenging because eating is a part of human behavior system and itself is a system. This BHI 2019 paper introduced a pilot study to examine the neurophysiological variation of food decision making with the goal of understanding the multifactorial aspects of eating behavior and further developing efficient treatment for persons with eating disorders. The experimental protocol was designed in virtual reality (VR) and real-life environments (e.g., buffet setting). 11 participants (age 18-25, average: 20.45±2.296) were recruited to equip with various body sensors (e.g., prefrontal cortex Functional near-infrared spectroscopy (fNIRS), Electrocardiography (ECG), galvanic skin response (GSR), eye movement and body motion) to capture their neural and physiological data as they were making food selections. In this exploratory study, we aimed to identify patterns of neural and physiological activity during food selection and the nutritional content of individuals’ final food selection choices. Findings revealed that the left inferior frontal gyrus demonstrated significant different activations when the subject chose high/low density food in both VR and real-life settings.