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

Algorithms

Frédérique Faïta-Aïnseba¹  Emigdio Z-Flores²  Leonardo Trujillo³  Pierrick Legrand⁴ 

[1] 351 Cours de la Libération, Bordeaux University, 33405 Talence, France;Departamento de Ingeniería Industrial, Tecnológico Nacional de México/IT de Tijuana, Calzada Del Tecnológico S/N, Fraccionamiento Tomas Aquino, Tijuana B.C., C.P. 22414, Mexico;Doctorado en Ciencias de la Ingeniería, Departamento de Ingeniería Eléctrica y Electrónica, Tecnológico Nacional de México/IT de Tijuana, Blvd. Industrial y Av. ITR Tijuana S/N, Mesa Otay, Tijuana B.C., C.P. 22500, Mexico;IMB UMR CNRS 5251—CQFD Team, Bordeaux University, INRIA, 200 Av de la Vieille Tour, 33405 Talence, France;

关键词: EEG;    classification;    genetic programming;    feature extraction;    mental states;   

DOI  :  10.3390/a13090221

来源: DOAJ

【 摘 要 】

The design of efficient electroencephalogram (EEG) classification systems for the detection of mental states is still an open problem. Such systems can be used to provide assistance to humans in tasks where a certain level of alertness is required, like in surgery or in the operation of heavy machines, among others. In this work, we extend a previous study where a classification system is proposed using a Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) for the classification of two mental states, namely a relaxed and a normal state. Here, we propose an enhanced feature extraction algorithm (Augmented Feature Extraction with Genetic Programming, or ${}^{+}$FEGP) that improves upon previous results by employing a Genetic-Programming-based methodology on top of the CSP. The proposed algorithm searches for non-linear transformations that build new features and simplify the classification task. Although the proposed algorithm can be coupled with any 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.

【 授权许可】

Unknown