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

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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