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Iliopoulos AC, Papasotiriou I. Functional Complex Networks Based on Operational Architectonics: Application on EEG-BCI for Imagined Speech. Neuroscience. 2021;doi: 10.1016/j.neuroscience.2021.11.045.
Iliopoulos, A. C., & Papasotiriou, I. (2021). Functional Complex Networks Based on Operational Architectonics: Application on EEG-BCI for Imagined Speech. Neuroscience, . https://doi.org/10.1016/j.neuroscience.2021.11.045
Iliopoulos, A C, and Papasotiriou, I. "Functional Complex Networks Based on Operational Architectonics: Application on EEG-BCI for Imagined Speech." Neuroscience vol. (2021). doi: https://doi.org/10.1016/j.neuroscience.2021.11.045
Iliopoulos AC, Papasotiriou I. Functional Complex Networks Based on Operational Architectonics: Application on EEG-BCI for Imagined Speech. Neuroscience. 2021 Dec 03; doi: 10.1016/j.neuroscience.2021.11.045. Epub 2021 Dec 03. PMID: 34871742.
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Neuroscience. 2021 Dec 03; doi: 10.1016/j.neuroscience.2021.11.045. Epub 2021 Dec 03.

Functional Complex Networks Based on Operational Architectonics: Application on EEG-BCI for Imagined Speech.

Neuroscience

A C Iliopoulos, I Papasotiriou

Affiliations

  1. Research Genetic Cancer Centre S.A. Industrial Area of Florina, 53100 Florina, Greece.
  2. Research Genetic Cancer Centre International GmbH, Zug, 6300, Switzerland.

PMID: 34871742 DOI: 10.1016/j.neuroscience.2021.11.045

Abstract

A new method for analyzing brain complex dynamics and states is presented. This method constructs functional brain graphs and is comprised of two pylons: a) Operational Architectonics (OA) concept of brain and mind functioning. b) Network Neuroscience. In particular, the algorithm utilizes OA framework for a non-parametric segmentation of EEGs, which leads to the identification of change points, namely abrupt jumps in EEG amplitude, called Rapid Transition Processes (RTPs). Subsequently, the time coordinates of RTPs are used for the generation of undirected weighted complex networks fulfilling a scale-free topology criterion, from which various network metrics of brain connectivity are estimated. These metrics form feature vectors, which can be used in machine learning algorithms for classification and/or prediction. The method is tested in classification problems on an EEG-based BCI data set, acquired from individuals during imagery pronunciation tasks of various words/vowels. The classification results, based on a Naïve Bayes classifier, show that the overall accuracies were found to be above chance level in all tested cases. This method was also compared with other state-of-the-art computational approaches commonly used for functional network generation, exhibiting competitive performance. The method can be useful to neuroscientists wishing to enhance their repository of brain research algorithms.

Copyright © 2021. Published by Elsevier Ltd.

Keywords: Brain complexity; Brain-Computer Interface (BCI); Machine Learning; Network Neuroscience; Operational Architectonics (OA); electroencephalography (EEG)

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