Date of Award
6-13-2018
Document Type
Thesis
Publisher
Santa Clara : Santa Clara University, 2018.
Department
Computer Engineering
First Advisor
Maya Ackerman
Abstract
The purpose of our project is to use EEG technology to combat stress in our daily lives. One of the most accessible EEG technologies that targets this challenge is the Muse headband, a wearable device that pairs with a phone application to help users train their brains to relax. The applications main goal is to help users train their brain to be more relaxed by monitoring and reporting their levels of stress. However, one of the shortcomings we noticed is that the constant notifications of how stressed we are actually adds to the level of stress as opposed to helping train our brains towards a more relaxed state.
In order to improve this solution, our program uses the live brain waves transmitted by the Muse headband and feedforward techniques to not only track brain users activity, but also help the user move towards a more relaxed state using music and binaural beats. While we werent able to test the system on an unbiased population due to time constraints, preliminary exploration on ourselves on both short term and longer term sessions shows that longer uses of our system led to more a relaxed state.
Recommended Citation
Capili, Jason; Hattori, Mark; and Naito, Maile, "Computational Music Biofeedback for Stress Relief" (2018). Computer Science and Engineering Senior Theses. 103.
https://scholarcommons.scu.edu/cseng_senior/103