Into the Wild: Deploying Brain and Physiological Sensing in Natural Environments to Enhance Wake and Sleep Cognitive Behavioral Studies

This thesis presents two experimental platforms for performing cognitive behavioral studies in natural settings: one for wake time and one for sleep. The equipment utilized today in behavioral and sleep labs is not very accessible, comfortable, portable, or simple to operate. The systems documented in this dissertation demanded the creation of novel wearables, sensors, signal processing, communications, and machine learning solutions that vastly outperformed current systems. The first platform introduced here is Entwine, a toolkit for behavioral researchers to create VR experiments. The first half of this toolkit includes Unity modules to help create a VR behavioral experiment. These modules are meant to lower the barrier of entry rather than replace Unity development, and they can be built on or modified by the user. I present a study that is able to identify the spatiotemporal dynamics between the autonomic nervous system (HR, EDA) and the central nervous system (Frontal and Parietal cortices) during a high cognitive demand task. I also explored how such a system can help measure and test the field of vision to evaluate retinal and early afferent visual pathways. The second contribution of this dissertation is the Fascia Ecosystem, which reinvents sleep studies using three key technologies. First, the Fascia Sleep Mask uses fabric-based sensing to collect polysomnogram-like data in a soft sleep mask. Second, the Fascia Hub lets a researcher or scientist give the patient audio and visual feedback and stimulation. This helps with sleep and dream research by allowing for interventions to be made. Finally, the machine learning API provides real-time sleep staging, spindles, and slow-wave saliency maps in the Fascia Portal, where sleep researchers can view patient signals and store experiment data. The presented work streamlines cognitive study procedures by introducing two novel solutions that will be shared with the scientific community. I have shown through user studies that these prototypes are easy to use and have the ability to significantly enhance cognitive research, diagnosis, and understanding of sleep structure.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Program in Media Arts and Sciences (Massachusetts Institute of Technology)

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Bernal Cubias, Guillermo Román

Advisor dc:contributor.advisor

• Maes, Pattie

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/152004

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/152004