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All Journal Enthusiastic : International Journal of Applied Statistics and Data Science
Hegde, Rajeshwari
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics

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Optimal Classification of Emotions from Electroencephalography (EEG) Signals M.P., Mahima; Reddy, Meghana; C., Rachita; Hegde, Rajeshwari
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 2 Issue 2, October 2022
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol2.iss2.art6

Abstract

It is incredibly challenging to build an intelligent algorithm for emotion recognition that can deliver high accuracy because electroencephalography (EEG) signals are not stationary, nonlinear, and noisy. First, decomposing the preprocessed EEG signals of the SEED dataset into five frequency bands: delta, theta, alpha, beta, and gamma, and then calculated their energy and entropy from the extracted features. Then Principal Component Analysis (PCA) method for feature reduction was performed.  It is important to note that different types of wavelets transform (db6, db5, etc.) were tested, and hyperparameter tuning of classification models was done to obtain optimal accuracy. The next step is classifying the emotions into three states:  -1(negative), 0(neutral), and +1(positive), and tested the dataset on two types of classification models, namely Random Forest and Support vector machine (SVM). SVM gives better performance compared to Random Forest with an accuracy of 80.74%.
Co-Authors C., Rachita M.P., Mahima Reddy, Meghana