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Validation of EEG Pre-processing Pipeline by Test-Retest Reliability

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Applied Computer Sciences in Engineering (WEA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 916))

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Abstract

Artifact removal and validation of pre-processing approaches remain as an open problem in EEG analysis. Cleaning data is a critical step in EEG analysis, per-formed to increase the signal-to-noise ratio and to eliminate unwanted artifacts. Methodologies commonly used for EEG pre-processing are: filtering, interpolation of bad channels, epoch segmentation, re-referencing, and elimination of physiological artifacts such as eye blinking or muscular activity. It is important to consider that the order and application of these steps affect signal quality for further analysis. In order to validate a pre-processing pipeline that can be considered in a clinical follow-up, this paper evaluated test-retest reliability of EEG recordings. EEG signals were acquired during eyes-closed resting state condition in two groups of healthy subjects with a follow-up of one and six months respectively. Signals were pre-processed with five different methodologies commonly used in literature. Test-retest reliability by intraclass correlation coefficient was calculated for power spectrum measures in each pre-processing approach and group. The results showed how test-retest reliability was significantly affected by pre-processing pipeline in both follow-ups. The pre-processing pipeline that com-bines robust reference to average and wavelet ICA improves the test-retest reliability.

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Acknowledgments

This work was supported by Vicerrectoría de Investigación of Universidad de Antioquia (CODI), Project “Neurofisiología y Neuropsicología en Enfermedad Ganglio Basal”, code PRG2014-768, Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS), announcement N. 757 Doctorados Nacionales, and the project “Identificación de Biomarcadores Preclínicos en Enfermedad de Alzheimer a través de un Seguimiento Longitudinal de la Actividad Eléctrica Cerebral en Poblaciones con Riesgo Genético”, code 111577757635, announcement 777-2017 of COLCIENCIAS.

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Authors and Affiliations

  1. Grupo de investigación en Bioinstrumentación e Ingeniería Clínica – GIBIC, Departamento de Bioingeniería, Facultad de Ingeniería, Universidad de Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia

    Jazmín Ximena Suárez-Revelo & John Fredy Ochoa-Gómez

  2. Grupo de Neurociencias de Antioquia – GNA, Grupo de Neuropsicología y conducta – GRUNECO, Universidad de Antioquia UdeA, Sede de Investigación Universitaria - SIU, Calle 62 No. 52-59, Medellín, Colombia

    Carlos Andrés Tobón-Quintero

Authors
  1. Jazmín Ximena Suárez-Revelo
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  2. John Fredy Ochoa-Gómez
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  3. Carlos Andrés Tobón-Quintero
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Corresponding author

Correspondence to Jazmín Ximena Suárez-Revelo .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Juan Carlos Figueroa-García

  2. Department of Industrial Engineering, University of Antioquia, Medellín, Colombia

    Juan G. Villegas

  3. Department of Electronics and Telecomunications Engineering, University of Antioquia, Medellín, Colombia

    Juan Rafael Orozco-Arroyave

  4. Department of Industrial Engineering, University of Antioquia, Medellín, Colombia

    Pablo Andres Maya Duque

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Suárez-Revelo, J.X., Ochoa-Gómez, J.F., Tobón-Quintero, C.A. (2018). Validation of EEG Pre-processing Pipeline by Test-Retest Reliability. In: Figueroa-García, J., Villegas, J., Orozco-Arroyave, J., Maya Duque, P. (eds) Applied Computer Sciences in Engineering. WEA 2018. Communications in Computer and Information Science, vol 916. Springer, Cham. https://doi.org/10.1007/978-3-030-00353-1_26

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  • DOI: https://doi.org/10.1007/978-3-030-00353-1_26

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