EEG Feature Extraction Using Genetic Programming for the Classification of Mental States
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{z-flores:2020:Algorithms,
-
author = "Emigdio Z-Flores and Leonardo Trujillo and
Pierrick Legrand and Frederique Faita-Ainseba",
-
title = "{EEG} Feature Extraction Using Genetic Programming for
the Classification of Mental States",
-
journal = "Algorithms",
-
year = "2020",
-
volume = "13",
-
number = "9",
-
keywords = "genetic algorithms, genetic programming",
-
ISSN = "1999-4893",
-
URL = "https://www.mdpi.com/1999-4893/13/9/221",
-
DOI = "doi:10.3390/a13090221",
-
abstract = "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 78.8percent accuracy, the best predictive
accuracy among tested classifiers, significantly
improving upon previously published results on the same
real-world dataset.",
-
notes = "also known as \cite{a13090221}",
- }
Genetic Programming entries for
Emigdio Z-Flores
Leonardo Trujillo
Pierrick Legrand
Frederique Faita-Ainseba
Citations