Abstract
Emotion recognition in artificial intelligence is a challenging factor. Recent years have seen a rise in interest in emotion analysis based on EEG signals due to many factors, including the availability of huge EEG datasets, developments in Brain-Computer interface (BCI) devices, and discoveries in machine learning. Due to its high temporal precision, portability, and relatively low cost of data collection, EEG is a viable option for researching the brain correlates of numerous cognitive activities, including emotion. The user's capacity to generate these signals is indicative of how well their brain processes information across a variety of cognitive, emotional, and physical domains. Important feature values must be extracted from these raw signals, which is still an essential part of the deployment process. In order to better understand the brain's activity, this study explains how to use principal component analysis (PCA) and power spectral entropy (PSE) to extract characteristics from an EEG signal. In this analysis, we evaluate how well-known classification algorithms fare when used on the recovered features: the support vector machine (SVM), the K-nearest neighbors (KNN), and the Naive Bayes (NB) classifier. All of the simulations were carried out in MATLAB using a HEADIT dataset. The results reveal that the suggested classifiers outperform the standard method in predicting emotional states.
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Babeetha, S., Sridhar, S.S. (2023). EEG Signal Feature Extraction Using Principal Component Analysis and Power Spectral Entropy for Multiclass Emotion Prediction. In: Shakya, S., Tavares, J.M.R.S., Fernández-Caballero, A., Papakostas, G. (eds) Fourth International Conference on Image Processing and Capsule Networks. ICIPCN 2023. Lecture Notes in Networks and Systems, vol 798. Springer, Singapore. https://doi.org/10.1007/978-981-99-7093-3_29
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DOI: https://doi.org/10.1007/978-981-99-7093-3_29
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