Detecting Human Memory Processes via Bio-Signals
Name
abdelrachman-monaabd-meng-bcs-2024-thesis.pdf
Description
Thesis PDF
Size
1.41 MB
Format
Adobe PDF
Checksum (MD5)
d2dc021299a41dc4cc22d619665575b5
Author(s)
Abdelrahman, Mona Magdy
Advisor(s)
Maes, Pattie
Date Issued
February 2024
Publisher
Massachusetts Institute of Technology
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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