Authors

Isaiah Garnett, Aspen (Shu) Yang, Ruolin Wu

Research Question

The goal of this project is to develop a controlled experimental framework using EEG, EDA sensors and VR to investigate the therapeutic effects of visual elements in built environments and quantify the relationship between urban features and human bio-reactions under different conditions. Integrating panoramic scenery, EEG recording and EDA watches in an indoor VR setting will allow us to build a replicable, controlled method of isolating environmental stressors and reducing the confounding effects commonly found in outdoor urban sensing studies.

Background

This study explores how specific visual elements in urban streetscapes impact cognitive and emotional responses related to stress. Building on prior work that used wearable sensors to collect physiological data during outdoor walks in NYC, this study addresses a key limitation: the lack of environmental control in real-world conditions to compare and quantify the therapeutic effects of urban features (e.., greenery, blue elements, urban form).

Previously selected NYC street routes will be rebuilt digitally using panoramic video captured by an Insta360 camera. Participants will experience these environments through a virtual reality headset in a controlled indoor setting, eliminating confounding factors such as physical exertion, noise, and weather. Participants will also experience these scenes, which have been enhanced in various ways. While participants are immersed in the VR scenes, EEG and EDA data will be collected and associated with attention and stress responses.

The project aims to identify which visual urban features — such as various forms of greenery — are associated with the mitigation of emotional stress in urban environments. This highly controlled, repeatable method will enable new insights into how cities shape mental well being for dwellers. The findings could help urban designers and policymakers create healthier environments that support mental health. And it is feasible, after utilizing existing equipment such as the Insta360 camera, EEG headsets, EDA health trackers and space. Data collection and analysis will follow a structured experimental protocol suitable for small sample insights.

The E2HW Lab at New York University leverages geospatial analysis and data science to delve into the profound consequences of human-environment interaction on public health. By combining expertise from various disciplines, the E2HW Lab is dedicated to making significant strides in promoting health equity and fostering positive changes in the environments that shape our wellbeing.

Methodology

This project requires the use of an EEG headset, EDA sensors, a VR headset, a 360-degree camera, and software tools including Python (e.g., JupyterLab), RStudio for spatial modeling, the Emotiv application for EEG data processing, Insta360 for panoramic video capture, and a VR content playback platform for immersive scene presentation.

Route Selection

1. Based on a comparative analysis of NYC street features — particularly greenery — this study will select three types of streetscapes: Streets with roadside trees; Streets without any greenery; Streets adjacent to open green parks; Streets along blue spaces (e.g., waterfronts); Streets incorporating both green and blue elements
2. Digital Reconstruction: Selected NYC street routes will be digitally reconstructed using 360-degree panoramic videos captured by Insta360 cameras and rendered in Unity, integrated with Virtual Reality for immersive experience delivery.
3. Participant Recruitment: The study aims to recruit 30 participants from NYU. Each participant will be equipped with EDA sensors and EEG headsets, and will wear VR headsets during the experiment.
4. Experimental Procedure: Participants will view the 3D panoramic streetscapes in Unity. During the session, wearable sensors will record physiological responses (e.g., skin conductance and brain activity) across the different route types.
5. Post-Session Survey: After the VR session, participants will complete a post-survey to provide subjective evaluations of their experience.
6. Data Analysis: Sensor data will be analyzed using signal processing and machine learning techniques to uncover insights into how different streetscape features impact human physiological responses.

Deliverables

In this project, students explore how the visual qualities of urban streetscapes relate to emotional and cognitive responses. They deepen their understanding of immersive research methods by combining VR and EEG to study stress responses in a controlled lab setting. Overall, students take an interdisciplinary approach:

1. Designing and conducting VR-based experiments to simulate real urban environments, and
2. Analyzing EEG and EDA data to quantify how visual elements influence emotional arousal and cognitive load.

This research process culminates in two deliverables:

1. Final report that presents the experimental findings and interprets the associations between urban form and stress, along with design recommendations and limitations
2. Demo or interactive visualization showcasing key findings, which may be used for public engagement or communication with stakeholders.

Data Sources

This project requires a combination of physiological, spatial, and contextual urban data. Students collect real-time EEG data and EDA data using wearable devices to measure human neural responses in different virtual urban environments. Publicly available datasets — such as NYC Open Data, NY State Open Data, and CDC Open Data — are used to support spatial analysis and provide demographic, health, and environmental context. Depending on the research focus, students may also incorporate large-scale external data sources, including Google Street View imagery, remote sensing data, and geotagged social media content, to enrich environmental characterization and behavioral insights.