Abstract
Over the past two decades, the research on EEG-based brain–computer interaction (BCI) made the BCI systems better and better, but building a robust BCI system for practical use is still a goal to achieve. To identify emotions of immobilized individuals, neuroscan machines like encephalography (EEG) is utilized. It uses the physiological signals available from EEG data extracted from the brain signals of immobilized persons and tries to determine the emotions, but these results vary from machine to machine, and there exists no standardization process which can identify the feelings of the brain diseased persons accurately. In this paper, a novel method is proposed, Skew Gaussian Mixture Model (SGMM) to have a complete emotion recognition system which can identify emotions more accurately in a noisy environment from both the healthy individuals and sick persons. The results of the proposed system surpassed the accuracy rates of traditional systems.
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Murali Krishna, N., Sirisha Devi, J., Vijaya Bhaskar Reddy, P., Nandyala, S.P. (2019). Emotion Recognition Using Skew Gaussian Mixture Model for Brain–Computer Interaction. In: Nayak, J., Abraham, A., Krishna, B., Chandra Sekhar, G., Das, A. (eds) Soft Computing in Data Analytics . Advances in Intelligent Systems and Computing, vol 758. Springer, Singapore. https://doi.org/10.1007/978-981-13-0514-6_30
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DOI: https://doi.org/10.1007/978-981-13-0514-6_30
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