Spiking Neural Network for On-line Cognitive Activity Classification Based on EEG Data

  • Stefan Schliebs
  • Elisa Capecci
  • Nikola Kasabov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8228)

Abstract

The paper presents a method for the classification of EEG data recorded in two cognitive scenarios, a relaxing and memory task. The method uses a reservoir of spiking neurons that are activated by the spatio-temporal EEG data. The states of the reservoir are periodically read out and classified producing in a continuous classification result over time. After suitable optimization of the model parameters, we achieve a test accuracy of 82% on a small data set. Future applications of the proposed model are discussed including its use for an early detection of a cognitive impairment such as in Alzheimers disease.

Keywords

Spiking Neural Networks Liquid State Machines Reservoir Computing EEG data classification Cognitive tasks 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Stefan Schliebs
    • 1
  • Elisa Capecci
    • 1
  • Nikola Kasabov
    • 1
  1. 1.KEDRIAuckland University of TechnologyNew Zealand

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