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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Anderson, Olin L."],"institution":null,"degree_name":"Master of Science (MS) in Computer Science","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-01-01T08:00:00Z","date_published":"2019-01-01T08:00:00Z","updated_at":"2026-07-24T02:13:00Z","subjects":["Biomedical Devices and Instrumentation","Other Computer Sciences"],"languages":[],"rights":["Access is available to all users"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://dc.ewu.edu/theses/545","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Anderson, Olin L."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS) in Computer Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Biomedical Devices and Instrumentation","Other Computer Sciences"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["Access is available to all users"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://dc.ewu.edu/theses/545"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Relaxed mental state detection using the Emotiv Epoc and Adaptive Threshold Algorithms"]}]}],"canonical_facts":{"dc:creator":["Anderson, Olin L."],"dc:description.abstract":["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.
"],"dc:identifier":["https://dc.ewu.edu/theses/545"],"dc:rights":["Access is available to all users"],"dc:subject":["Biomedical Devices and Instrumentation","Other Computer Sciences"],"dc:title":["Relaxed mental state detection using the Emotiv Epoc and Adaptive Threshold Algorithms"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science (MS) in Computer Science"]},"updated_at":"2026-07-24T02:13:00Z"}