A methodology for synthesizing interdependent multichannel EEG data with a comparison among three blind source separation techniques

Ahmed Al-Ani, Ganesh R. Naik, Hussein A. Abbass

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we introduce a novel method for constructing synthetic, but realistic, data of four Electroencephalography (EEG) channels. The data generation technique relies on imitating the relationships between real EEG data spatially distributed over a closed-circle. The constructed synthetic dataset establishes ground truth that can be used to test different source separation techniques. The work then evaluates three projection techniques – Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Canonical Component Analysis (CCA) – for source identification and noise removal on the constructed dataset. These techniques are commonly used within the EEG community. EEG data is known to be highly sensitive signals that get affected by many relevant and irrelevant sources including noise and artefacts. Since we know ground truth in a synthetic dataset, we used differential evolution as a global optimisation method to approximate the “ideal” transform that need to be discovered by a source separation technique. We then compared this transformation with the findings of PCA, ICA and CCA. Results show that all three techniques do not provide optimal separation between the noisy and relevant components, and hence can lead to loss of useful information when the noisy components are removed.

Original languageEnglish
Title of host publicationNeural Information Processing
Subtitle of host publication22ndInternational Conference, ICONIP 2015, November 9-12, 2015, Proceedings, Part IV
EditorsSabri Arik, Tingwen Huang, Weng Kin Lai, Qingshan Liu
Place of PublicationSwitzerland
PublisherSpringer, Cham
Pages154-161
Number of pages8
ISBN (Electronic)9783319265612
ISBN (Print)9783319265605
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event22nd International Conference on Neural Information Processing, ICONIP 2015 - Istanbul, Turkey
Duration: 9 Nov 201512 Nov 2015

Publication series

NameLecture Notes in Computer Science
Volume9492
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Neural Information Processing, ICONIP 2015
CountryTurkey
CityIstanbul
Period9/11/1512/11/15

Keywords

  • Artefact removal
  • CCA
  • Differential evolution
  • ICA
  • Optimal projection
  • PCA
  • Synthetic multichannel EEG

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  • Cite this

    Al-Ani, A., Naik, G. R., & Abbass, H. A. (2015). A methodology for synthesizing interdependent multichannel EEG data with a comparison among three blind source separation techniques. In S. Arik, T. Huang, W. K. Lai, & Q. Liu (Eds.), Neural Information Processing: 22ndInternational Conference, ICONIP 2015, November 9-12, 2015, Proceedings, Part IV (pp. 154-161). (Lecture Notes in Computer Science; Vol. 9492). Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_19