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Classify four imagined objects with EEG signals

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Abstract

EEG signals contain information directly related to cognitive activity. This paper presents a method to classify the images a person imagines via the information provided by the EEG signals. The images relating to the objects ‘tree’, ‘house’, ‘plane’ and ‘dog’ have been reconstructed. We have used a convolutional neural networks to obtain the reconstruction of the images and a genetic algorithm to find the parameters of this network. The results obtained have been evaluated by means of a Chebychev metric to compare the images, and it shows that the reconstruction is performed with a success of 57% over chance, with an accuracy in the classification of 60% and a kappa value of 0.40, demonstrating that the classification of five mental states where four of them come from the visual imagery, is possible.

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Acknowledgements

Thank all the people who have volunteered to make the EEG signals registers, without them this work would not have been possible. This work was partially funded by the project TIN2017-88515-C2-2-R from Ministerio de Ciencia, Innovación y Universidades, Spain.

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Authors and Affiliations

  1. ViRVIG - University of Girona, Girona, Catalonia, Spain

    Fabio R. Llorella Costa & Gustavo Patow

  2. BMI-University Miguel Hernández, Elche, Valencia, Spain

    Eduardo Iáñez & José M. Azorín

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  1. Fabio R. Llorella Costa
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  2. Eduardo Iáñez
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  3. José M. Azorín
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  4. Gustavo Patow
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Correspondence to Fabio R. Llorella Costa.

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Costa, F.R.L., Iáñez, E., Azorín, J.M. et al. Classify four imagined objects with EEG signals. Evol. Intel. 15, 1657–1666 (2022). https://doi.org/10.1007/s12065-021-00577-y

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