The focus of this research is on the development of a real time application that uses a low cost EEG headset to measure a player's state of mind while they play a video game. Using data collected using the Emotiv EPOC headset, various EEG processing techniques are tested to find ways of measuring a person's engagement and arousal levels. The ability to measure a person's engagement and arousal levels provide an opportunity to develop a model that monitor a person's flow while playing video games. Identifying when certain events occur, like when the player dies, will make it easier to identify when a player has left a state of flow. The real time application Brainwave captures data from the wireless Emotiv EPOC headset. Brainwave converts the raw EEG data into more meaningful brainwave band frequencies. Utilizing the brainwave frequencies the program trains multiple machine learning algorithms with data designed to identify when the player dies. Brainwave runs while the player plays through a video gaming monitoring their engagement and arousal levels for changes that cause the player to leave a state of flow. Brainwave reports to researchers and developers when the player dies along with the identification of the players exit of the state of flow.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc955121 |
| Date | 12 1900 |
| Creators | McMahan, Timothy |
| Contributors | Parberry, Ian, Parsons, Thomas D., Akl, Robert, Mikler, Armin, Renka, Robert |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | x, 99 pages : illustrations, Text |
| Rights | Public, McMahan, Timothy, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
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