EEG Feature Extraction Using Genetic Programming for the Classification of Mental States - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Algorithms Year : 2020

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.

Dates and versions

hal-02943474 , version 1 (19-09-2020)

Identifiers

Cite

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⟩

Collections

CNRS INRIA INRIA2
33 View
0 Download

Altmetric

Share

Gmail Facebook X LinkedIn More