Research Article
Towards Mobile-Based Preprocessing Pipeline for Electroencephalography (EEG) Analyses: The Case of Tinnitus
@INPROCEEDINGS{10.1007/978-3-030-70569-5_5, author={Muntazir Mehdi and Lukas Hennig and Florian Diemer and Albi Dode and R\'{y}diger Pryss and Winfried Schlee and Manfred Reichert and Franz J. Hauck}, title={Towards Mobile-Based Preprocessing Pipeline for Electroencephalography (EEG) Analyses: The Case of Tinnitus}, proceedings={Wireless Mobile Communication and Healthcare. 9th EAI International Conference, MobiHealth 2020, Virtual Event, November 19, 2020, Proceedings}, proceedings_a={MOBIHEALTH}, year={2021}, month={7}, keywords={Healthcare Mobile health Smartphone apps Mobile apps Tinnitus EEG Brain imaging Brain Computer Interfaces}, doi={10.1007/978-3-030-70569-5_5} }- Muntazir Mehdi
Lukas Hennig
Florian Diemer
Albi Dode
Rüdiger Pryss
Winfried Schlee
Manfred Reichert
Franz J. Hauck
Year: 2021
Towards Mobile-Based Preprocessing Pipeline for Electroencephalography (EEG) Analyses: The Case of Tinnitus
MOBIHEALTH
Springer
DOI: 10.1007/978-3-030-70569-5_5
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.
