Emotions have significant importance in human life in learning, decision-making, daily interaction, and perception of the surrounding environment. Hence, it has become very essential to detect and recognize a person's emotional states and to build a connection between humans and computers. This process is called brain-computer interaction (BCI) and is a vast field of research in neuroscience. Hence, in the past few years, emotion recognition has gained adequate attention in the research community. In this thesis, an emotion recognition system is designed and analyzed using EEG signals. Several existing feature extraction techniques are studied, analyzed, and implemented to extract features from the EEG signals. An SVM classifier is used to classify the features into various emotional states. Four emotional states are detected, namely, happy, sad, anger, and relaxed state. The model is tested, and simulation results are presented with an interpretation. Furthermore, this study has mentioned and discussed the efficacy of the results achieved. The findings from this study could be beneficial in developing emotion-sensitive technologies, such as augmented modes of communication for severely disabled individuals who are unable to communicate their feelings directly.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc1944242 |
| Date | 05 1900 |
| Creators | Choudhary, Sairaj Mahesh |
| Contributors | Namuduri, Kamesh, Mahbub, Ifana, Mehta, Gayatri |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | Text |
| Rights | Public, Choudhary, Sairaj Mahesh, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
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