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Front. Neuroinform. | doi: 10.3389/fninf.2019.00020

MEEGIPS - a Modular EEG Investigation and Processing System for visual and automated detection of high frequency oscillations

  • 1Paracelsus Medizinische Privatuniversität, Salzburg, Austria

High frequency oscillations (HFOs) are electroencephalographic correlates of brain activity detectable in a frequency range above 80 Hz. They co-occur with physiological processes such as saccades, movement execution, and memory formation, but are also also related to pathological processes in patients with epilepsy. Localization of the seizure onset zone, and, more specifically, of the to-be resected area in patients with refractory epilepsy seems to be supported by the detection of HFOs. The visual identification of HFOs is very time consuming with approximately 8 hours for 10min and 20 channels. Therefore, automated detection of HFOs is highly warranted. So far, no software for visual marking or automated detection of HFOs meets the needs of everyday clinical practice and research.

In the context of the currently available tools and for the purpose of related local HFO study activities we aimed at converging the advantages of clinical and experimental systems by designing and developing a comprehensive and extensible software framework for HFO analysis that, on the one hand, focuses on the requirements of clinical application and, on the other hand, facilitates the integration of experimental code and algorithms.

The development project included the definition of use cases, specification of requirements, software design, implementation, and integration. The work comprised the engineering of component-specific requirements, component design, as well as component- and integration-tests. A functional and tested software package is the deliverable of this activity.

The project MEEGIPS, a Modular EEG Investigation and Processing System for visual and automated detection of HFOs, introduces a highly user friendly software that includes five of the most prominent automated detection algorithms. Implementation of further algorithms is facilitated by the modular software architecture.

Keywords: EEG, HFO, automated HFO detection, High frequency oscillations, Epilepsy, MEG/EEG software

Received: 07 Jul 2018; Accepted: 11 Mar 2019.

Edited by:

Arjen Van Ooyen, VU University Amsterdam, Netherlands

Reviewed by:

Stavros I. Dimitriadis, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, United Kingdom
Christos Papadelis, Harvard Medical School, United States  

Copyright: © 2019 Höller, Trinka and Höller. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Yvonne Höller, Paracelsus Medizinische Privatuniversität, Salzburg, Salzburg, 5020, Salzburg, Austria,