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EEG Feature Extraction Using Genetic Programming for the Classification of Mental States

Abstract : The design of efficient electroencephalogram (EEG) classification systems for the detectionof mental states is still an open problem. Such systems can be used to provide assistance to humansin tasks where a certain level of alertness is required, like in surgery or in the operation of heavymachines, among others. In this work, we extend a previous study where a classification system isproposed using a Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) for theclassification of two mental states, namely a relaxed and a normal state. Here, we propose an enhancedfeature extraction algorithm (Augmented Feature Extraction with Genetic Programming, or+FEGP)that improves upon previous results by employing a Genetic-Programming-based methodologyon top of the CSP. The proposed algorithm searches for non-linear transformations that build newfeatures and simplify the classification task. Although the proposed algorithm can be coupled withany classifier, LDA achieves 78.8% accuracy, the best predictive accuracy among tested classifiers,significantly improving upon previously published results on the same real-world dataset.
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Contributor : Pierrick Legrand Connect in order to contact the contributor
Submitted on : Saturday, September 19, 2020 - 5:26:48 PM
Last modification on : Friday, August 5, 2022 - 2:50:31 PM

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Emigdio Z-Flores, Leonardo Trujillo, Pierrick Legrand, Frédérique Faïta-Aïnseba. EEG Feature Extraction Using Genetic Programming for the Classification of Mental States. Algorithms, 2020, 13 (9), pp.221. ⟨10.3390/a13090221⟩. ⟨hal-02943474⟩



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