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Feature Selection of EEG Oscillatory Activity Related to Motor Imagery Using a Hierarchical Genetic Algorithm
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-3425-3837
Mälardalen University, School of Innovation, Design and Engineering.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-8174-1067
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-9857-4317
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2019 (English)In: 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 87-94Conference paper, Published paper (Refereed)
Abstract [en]

Motor Imagery (MI) classification from neural activity is thought to represent valuable information that can be provided as real-time feedback during rehabilitation after for example a stroke. Previous studies have suggested that MI induces partly subject-specific EEG activation patterns, suggesting that individualized classification models should be created. However, due to fatigue of the user, only a limited number of samples can be recorded and, for EEG recordings, each sample is often composed of a large number of features. This combination leads to an undesirable input data set for classification. In order to overcome this constraint, we propose a new methodology to create and select features from the EEG signal in two steps. First, the input data is divided into different windows to reduce the cardinality of the input. Secondly, a Hierarchical Genetic Algorithm is used to select relevant features using a novel fitness function which combines the data reduction with a correlation feature selection measure. The methodology has been tested on EEG oscillatory activity recorded from 6 healthy volunteers while they performed an MI task. Results have successfully proven that a classification above 75% can be obtained in a restrictive amount of time (0.02 s), reducing the number of features by almost 90%.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2019. p. 87-94
Keywords [en]
EEG Signal, Hierarchical Genetic Algorithm, Motor Imagery, Classification (of information), Data reduction, Genetic algorithms, Input output programs, Neurons, Activation patterns, Classification models, Correlation features, EEG signals, Healthy volunteers, Real-time feedback, Feature extraction
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-46543DOI: 10.1109/CEC.2019.8789948ISI: 000502087100013Scopus ID: 2-s2.0-85071317681ISBN: 9781728121536 (print)OAI: oai:DiVA.org:mdh-46543DiVA, id: diva2:1379391
Conference
2019 IEEE Congress on Evolutionary Computation, CEC 2019, 10 June 2019 through 13 June 2019
Available from: 2019-12-17 Created: 2019-12-17 Last updated: 2020-01-02Bibliographically approved

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Leon, MiguelBallesteros, JoaquinTidare, JonatanXiong, NingÅstrand, Elaine

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Leon, MiguelBallesteros, JoaquinTidare, JonatanXiong, NingÅstrand, Elaine
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Embedded SystemsSchool of Innovation, Design and Engineering
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