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Towards Mobile-Based Preprocessing Pipeline for Electroencephalography (EEG) Analyses: The Case of Tinnitus

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Wireless Mobile Communication and Healthcare (MobiHealth 2020)

Abstract

Recent developments in Brain-Computer Interfaces (BCI)—technologies to collect brain imaging data—allow recording of Electroencephalography (EEG) data outside of a laboratory setting by means of mobile EEG systems. Brain imaging has been pivotal in understanding the neurobiological correlates of human behavior in many complex disorders. This is also the case for tinnitus, a disorder that causes phantom noise sensations in the ears in absence of any sound source. As studies have shown that tinnitus is also influenced by complexities in non-auditory brain areas, mobile EEG can be a viable solution in better understanding the influencing factors causing tinnitus. Mobile EEG will become even more useful, if real-time EEG analysis in mobile experimental environments is enabled, e.g., as an immediate feedback to physicians and patients or in undeveloped areas where a laboratory setup is unfeasible. The volume and complexity of brain imaging data have made preprocessing a pertinent step in the process of analysis, e.g., for data cleaning and artifact removal. We introduce the first smartphone-based preprocessing pipeline for real-time EEG analysis. More specifically, we present a mobile app with a rudimentary EEG preprocessing pipeline and evaluate the app and its resource consumption underpinning the feasibility of smartphones for EEG preprocessing. Our proposed approach will allow researchers to collect brain imaging data of tinnitus and other patients in real-world environments and everyday situations, thereby collecting evidence for previously unknown facts about tinnitus and other conditions.

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Notes

  1. 1.

    https://www.brainproducts.com/productdetails.php?id=63 Accessed: 15/06/2020.

  2. 2.

    https://github.com/berndporr/iirj Accessed: 15/06/2020.

  3. 3.

    https://github.com/NeuroTechX/eeg-101 Accessed: 15/06/2020.

  4. 4.

    https://github.com/PhilJay/MPAndroidChart Accessed: 15/06/2020.

  5. 5.

    https://consumer.huawei.com/de/support/phones/p20-lite/ Accessed: 15/06/2020.

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Acknowledgment

This publication is a result of research supported by funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement number 722064 (European School for Interdisciplinary Tinnitus Research, ESIT) [29]. We would also like to acknowledge Brain Products GmbH (https://www.brainproducts.com/ Accessed: 15/06/2020) for their help and support while working with the LiveAmp 16 EEG amplifier.

Author information

Authors and Affiliations

  1. Institute of Distributed Systems, Ulm University, Ulm, Germany

    Muntazir Mehdi, Lukas Hennig, Florian Diemer & Franz J. Hauck

  2. Institute of Databases and Information Systems, Ulm University, Ulm, Germany

    Albi Dode & Manfred Reichert

  3. Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany

    Rüdiger Pryss

  4. Clinic and Policlinic for Psychiatry and Psychotherapy, Regensburg, Germany

    Winfried Schlee

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  1. Muntazir Mehdi
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  2. Lukas Hennig
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Correspondence to Muntazir Mehdi .

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Editors and Affiliations

  1. School of Computer Science, University of St Andrews, St Andrews, UK

    Juan Ye

  2. School of Computer Science and Information Technology, University College Dublin, Dublin, Dublin, Ireland

    Michael J. O'Grady

  3. Department of Computer Science, University of Milan, Milan, Italy

    Gabriele Civitarese

  4. Faculty of Computer Science and Electrical Engineering, University of Rostock, Rostock, Germany

    Kristina Yordanova

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Mehdi, M. et al. (2021). Towards Mobile-Based Preprocessing Pipeline for Electroencephalography (EEG) Analyses: The Case of Tinnitus. In: Ye, J., O'Grady, M.J., Civitarese, G., Yordanova, K. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 362. Springer, Cham. https://doi.org/10.1007/978-3-030-70569-5_5

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  • DOI: https://doi.org/10.1007/978-3-030-70569-5_5

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  • Print ISBN: 978-3-030-70568-8

  • Online ISBN: 978-3-030-70569-5

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