Emotion states greatly influence many areas in our daily lives, such as: learning, decision making and interaction with others. Therefore, the ability to detect and recognize one’s emotional states is essential in intelligence Human Machine Interaction (HMI). In this thesis, a pattern classification framework was developed to sense and communicate emo- tion changes expressed by the Central Nervous System (CNS) through the use of EEG signals. More specifically, an EEG-based subject-dependent affect recognition system was developed to quantitatively measure and categorize three affect states: Positively excited, neutral and negatively excited. Several existing feature extraction algorithms and classifiers were researched, analyzed and evaluated through a series of classification simulations using a publicly available emotion-based EEG database. Simulation results were presented followed by an interpretation discussion.
The findings in this thesis can be useful for the design of affect sensitive applications such as augmented means of communication for severely disabled people that cannot directly express their emotions. Furthermore, we have shown that with significantly reduced number of channels, classification rates maintained a level that is feasible for emotion recognition. Thus current HMI paradigms to integrate consumer electronics such as smart hand-held device with commercially available EEG headsets is promising and will significantly broaden the application cases.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/42884 |
| Date | 27 November 2013 |
| Creators | Xu, Haiyan |
| Contributors | Plataniotis, Konstantinos N. |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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