Abstract
Spiking neural networks (SNN) have been successfully applied in pattern classification problems. However, their performance for solving complex problems such as electroencephalography (EEG) classification has not been widely assessed. It is necessary to consider new approaches to select relevant information and for training SNN in order to improve their accuracy when applied to complex data classification. In this paper, we present a novel channel selection and classification method based on SNN trained with Particle Swarm Optimization (PSO) for the classification of EEG signals associated to motor imagery. The proposed method was able to correctly identify the most relevant channels for different motor imagery tasks. The SNN trained with PSO achieved good classification performances for a well-studied public database using a minimal number of EEG channels, showing advantages against other approaches, regarding both performance and system requirements.
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Acknowledgments
The authors thank CONACYT and Universidad La Salle for the economic support under grant SALUD-2015-2-262061 and NEC-03/15, respectively.
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Carino-Escobar, R., Cantillo-Negrete, J., Vazquez, R.A., Gutierrez-Martinez, J. (2016). Spiking Neural Networks Trained with Particle Swarm Optimization for Motor Imagery Classification. In: Tan, Y., Shi, Y., Li, L. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9713. Springer, Cham. https://doi.org/10.1007/978-3-319-41009-8_26
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DOI: https://doi.org/10.1007/978-3-319-41009-8_26
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