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Classification of motor imagery electroencephalography signals using spiking neurons with different input encoding strategies

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Abstract

Motor imagery-based brain–computer interfaces decode users’ intentions from the electroencephalogram; however, poor spatial resolution makes automatic recognition of these intentions a challenging task. New classification approaches with low computational costs and high classification performances need to be developed in order to increase the number of users benefitted by these systems. On the other hand, spiking neuron models, which are mathematical abstractions of real neurons, have shown good performances in several classification tasks, making these models suitable for motor imagery classification. In this work, two different encoding strategies for spiking neuron models, applied to the classification of motor imagery time–frequency features of stroke patients and healthy subjects, were evaluated. Classification performances and computational costs of spiking neuron models were compared against those of linear discriminant analysis, support vector machines and artificial neural networks. Results showed that a time-varying encoding strategy is more suitable for motor imagery classification, and its implementation computational cost is low. Therefore, a spiking neuron model with a time-varying encoding strategy could increase the number of potential users of brain–computer interfaces.

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Acknowledgements

The authors would like to thank Consejo Nacional de Ciencia y Tecnología and Universidad La Salle for the economic support under Grant SALUD-2015-2-262061 and NEC-03/15, respectively.

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Authors and Affiliations

  1. Technological Research Subdirection, Instituto Nacional de Rehabilitación, 14389, Mexico City, Mexico

    Ruben I. Carino-Escobar, Jessica Cantillo-Negrete & Josefina Gutierrez-Martinez

  2. Intelligent Systems Group, Faculty of Engineering, La Salle University, 06140, Mexico City, Mexico

    Ruben I. Carino-Escobar & Roberto A. Vazquez

Authors
  1. Ruben I. Carino-Escobar
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  2. Jessica Cantillo-Negrete
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  3. Josefina Gutierrez-Martinez
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Correspondence to Ruben I. Carino-Escobar.

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Carino-Escobar, R.I., Cantillo-Negrete, J., Gutierrez-Martinez, J. et al. Classification of motor imagery electroencephalography signals using spiking neurons with different input encoding strategies. Neural Comput & Applic 30, 1289–1301 (2018). https://doi.org/10.1007/s00521-016-2767-9

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  • DOI: https://doi.org/10.1007/s00521-016-2767-9

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