Detecting Human Memory Processes via Bio-Signals

Bio signals, such as eye movement data, photoplethysmography (PPG), and electrodermal activity (EDA), can provide insight into various cognitive states. Previous work has shown that eye movements along with other bio-signals differ when viewing familiar versus unfamiliar faces. Signals such as heart rate (derived from PPG) and skin conductance (derived from EDA) have also been previously evaluated to have correlations with different states of memory. In this study, we collected simultaneous pupillary, PPG, and EDA signals while participants (n=32) transitioned between several cognitive states (learning, recognition, and recall). Using this data, we propose multi-modal, machine learning methods to predict and evaluate whether a user is in a cognitive state of learning, recognition, or recall. We will discuss the differences observed in the data between these cognitive states, as well as next steps and applications for this model.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Abdelrahman, Mona Magdy

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/1721.1/155895

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/155895