Efficient Mental Arithmetic Classification Using Approximate Entropy Features and Machine Learning Classifiers

Year 2023, Volume: 5 Issue: 3, 109 - 120, 31.12.2023

Saif Al-jumaili

Abstract

In the current era, detecting mental workload is one of the most important methods used to
determine the mental state of humans, which in turn helps determine whether there is an issue in
the brain. Machine learning became the most used field used by researchers due to its accurate
ability to deal with and analyze the state of the brain. In this study, machine learning was used to
classify the Mental Arithmetic Task Performance (before and after) using EEG signals. Initially, as a preprocessing method, due to the variance of the signal received from the brain, we divide
the signal into Sub-bands namely alpha, beta, gamma, theta, and delta for artifact removal. Then
we applied Approximate entropy (ApEn) to extract features from the signals. Next, the deduced
features were applied to 8 different types of classification methods, which are ensemble classifier, k-nearest neighbor (KNN), linear discriminate (LD), support vector machine (SVM), decision
trees (DT), logistic regression (LR), neural network (NN), and quadratic discriminate (QD). We
have achieved an optimal result using ES, furthermore, we compared our work with other papers
in the literature, and the results outperformed them

Keywords

electroencephalogram (EEG), machine learning (ML), ES, SVM, KNN, LD, LR, DT, NN, QD.

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