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Genetic algorithms based LSSVM for EEG fatigue multi-classification
ID：121 View Protection：ATTENDEE Updated Time：2022-05-19 16:19:01 Hits：390 Poster Presentation

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Abstract
In order to improve the classification accuracy of multiclassification of EEG fatigue data, a genetic algorithm based least squares support vector machine algorithm (GA-LSSVM)  is proposed in this paper. Firstly, the hyperparameters σ (kernel function width) and γ (the regularization parameter) of LSSVM are optimized by GA to obtain the GA-LSSVM algorithm model. Secondly the electroence-phalographic (EEG) signals use SEED-VIG fatigue data with 17 channels five frequency bands and 4 features. The data are divided into training set and test set according to 7:3 proportion to train SVM model and verify the presented algorithm. Experiments evidence that the GA-LSSVM algorithm improves the classification accuracy of EEG signals. 
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Speaker
LiYuxiang
Qingdao University

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Important Date

  • Conference Date

    May 27

    2022

    to

    May 29

    2022

  • Feb 28 2022

    Draft paper submission deadline

  • May 29 2022

    Registration deadline

  • Jun 22 2022

    Contribution Submission Deadline

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IEEE Beijing Section
China Electrotechnical Society
Southeast University
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IEEE Nanjing Section
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