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Abstract

The goal was to evaluate the performance of the state-of-the-art algorithms. A secondary goal was to try to improve upon the result of a method that was used in a study similar to the one used in this work. This paper presented the building of multi-state datasets relating to eye behaviors and facial expressions. Signals were recorded and stored by the connection of a channel-less mobile device. Z-score, max-min normalization techniques were used to optimize data. The cross-validation technique divided the data into training/testing segments. The features of the electrical brain signals (delta, theta, alpha and beta band) were analyzed by the Daubechies wavelet transform method. The extracted time and frequency domain features calculate total energy, detailed component energy, approximate component energy, relative energy. Three algorithms, support vector machine, k-nearest neighbor, and ensemble algorithm, were used to develop into 17 models to optimize the classification efficiency of the machine learning algorithms. Parameters of these models were surveyed and optimized to propose a best classification one for the Data-021 dataset. The Subspace ensemble model was proposed because its model efficiency was more than 87,7%.



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Article Details

Issue: Vol 6 No 2 (2022): Vol 6, Issue 2, 2022: Under publishing
Page No.: In press
Published: Jun 3, 2022
Section: Original Research
DOI: https://doi.org/10.32508/stdjns.v6i2.1157

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Creative Commons License

Copyright: The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 How to Cite
Võ, T., Nguyễn, Q., Nguyễn, P., & Huỳnh, T. (2022). Using wavelet transform for features extraction and machine learning algorithms to classify the facial expression by eeg signals. Science and Technology Development Journal - Natural Sciences, 6(2), In press. https://doi.org/https://doi.org/10.32508/stdjns.v6i2.1157

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