Abstract

An emotion is a state of one's mind that derives from situations, mood, etc. Understanding people's emotions is a complex problem. People express their emotions using facial expressions or via voice modulations; also, they use emotion regulation strategies such as cognitive reappraisal and expressive suppression in certain instances. Emotion regulation is a method by which we alter our emotions by changing how we express and perceive different situations. Understanding these emotional regulations helps to interact better, communicate and provide care. In this research, we analyze, visualize the differences and distinguish the emotions of disgust and humor with and without emotion regulation strategies. This is done by analyzing electrocardiography data (EKG), electroencephalogram data (EEG), galvanic skin response (GSR), and facial action units (FAU) data with machine learning algorithms such as convolutional neural networks (CNN), gated recurrent units (GRU), long short term memory (LSTM) and support vector machines (SVM). The data is collected by exposing participants (N = 21) to an inductive emotion video in a controlled environment.

Library of Congress Subject Headings

Emotions--Data processing; Cognition--Data processing; Electrocardiography--Data processing; Electroencephalography--Data processing; Galvanic skin response--Data processing; Facial expression--Data processing; Neural networks (Computer science); Machine learning; Support vector machines

Publication Date

3-2021

Document Type

Thesis

Student Type

Graduate

Degree Name

Computer Science (MS)

Department, Program, or Center

Computer Science (GCCIS)

Advisor

Ifeoma Nwogu

Advisor/Committee Member

Carol J Romanowski

Advisor/Committee Member

Joseph Baschnagel

Campus

RIT – Main Campus

Plan Codes

COMPSCI-MS

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