Relaxed mental state detection using the Emotiv Epoc and Adaptive Threshold Algorithms

<p>The electroencephalogram (EEG) has proven to be useful in a wide variety of applications, including: diagnosis of mental disorders, psychological research, neurofeedback, and brain-computer interfacing. Most such applications of the EEG benefit from an ability to automatically detect when the subject is in a relaxed state. Recently, inexpensive and relatively easy to use EEG systems, with multiple electrodes, have become available at prices comparable to cellular phones or game machines. This project’s purpose is to investigate the feasibility of real-time classification of a subject's relaxation state using one such consumer-grade EEG system, the Emotiv Epoc. The subject's state is classified as relaxed or non-relaxed by monitoring the EEG signals over the occipital brain region and monitoring alpha wave activity. Said activity is characterized using an adaptive subject-specific threshold algorithm. Different variations of the threshold algorithm were investigated and their performance was compared using receiver operating characteristic graphs.</p>

Name thesis:degree_name

Master of Science (MS) in Computer Science

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Year

2019

Author dc:creator

• Anderson, Olin L.

• Biomedical Devices and Instrumentation
• Other Computer Sciences

Statement dc:rights

• Access is available to all users

Repository record dc:identifier

https://dc.ewu.edu/theses/545

OAI identifier oai:identifier

oai:dc.ewu.edu:theses-1546