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Feature extraction with GMDH-type neural networks for EEG-based person identification

  • Vitaly Schetinin
    ,
  • Livija Jakaite
    ,
  • Ndifreke Nyah
    ,
  • Dusica Novakovic
    ,
  • Wojtek Krzanowski
Research Output: Contribution to journal Article Peer-review

Open access

Abstract

The brain activity observed on EEG electrodes is influenced by volume conduction and functional connectivity of a person performing a task. When the task is a biometric test the EEG signals represent the unique “brain print”, which is defined by the functional connectivity that is represented by the interactions between electrodes, whilst the conduction components cause trivial correlations. Orthogonalization using autoregressive modeling minimizes the conduction components, and then the residuals are related to features correlated with the functional connectivity. However, the orthogonalization can be unreliable for high-dimensional EEG data. We have found that the dimensionality can be significantly reduced if the baselines required for estimating the residuals can be modeled by using relevant electrodes. In our approach, the required models are learnt by a Group Method of Data Handling (GMDH) algorithm which we have made capable of discovering reliable models from multidimensional EEG data. In our experiments on the EEG-MMI benchmark data which include 109 participants, the proposed method has correctly identified all the subjects and provided a statistically significant (p<0.01) improvement of the identification accuracy. The experiments have shown that the proposed GMDH method can learn new features from multi-electrode EEG data, which are capable to improve the accuracy of biometric identification.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 1750064

Journal (Volume, Issue Number)

International Journal of Neural Systems (Volume 28, Issue 6)

Publication milestones

  • Accepted/In press - 23/12/2017
  • Published - 26/01/2018

Publication status

Published - 26/01/2018

ISSN

0129-0657

External Publication IDs

  • handle.net: 10547/622506
  • Scopus: 85040916045

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