Towards a software architecture for neurophysiological experiments

Constantina Ioannou, Ekkart Kindler, Per Bækgaard, Shazia Saqid, Barbara Weber

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

133 Downloads (Pure)

Abstract

Despite their wide adoption for conducting experiments in numerous domains, neurophysiological measurements often are time consuming and challenging to interpret because of the inherent complexity of deriving measures from raw signal data and mapping measures to theoretical constructs. While significant
efforts have been undertaken to support neurophysiological experiments, the existing software solutions are non-trivial to use because often these solutions are
domain specific or their analysis processes are opaque to the researcher. This paper proposes an architecture for a software platform that supports experiments
with multi-modal neurophysiological tools through extensible, transparent and repeatable data analysis and enables the comparison between data analysis processes to develop more robust measures. The identified requirements and the proposed architecture are intended to form a basis of a software platform capable of
conducting experiments using neurophysiological tools applicable to various domains.
Original languageEnglish
Title of host publicationProceedings of the NeuroIS Retreat 2019
PublisherSpringer
Publication date2019
Pages155-163
ISBN (Print)9783030281434
DOIs
Publication statusPublished - 2019
EventNeuroIS Retreat 2019
- Schloss Wilhelminenberg, Vienna, Austria
Duration: 4 Jun 20196 Jun 2019
http://www.neurois.org/

Conference

ConferenceNeuroIS Retreat 2019
LocationSchloss Wilhelminenberg
CountryAustria
CityVienna
Period04/06/201906/06/2019
Internet address

Keywords

  • Neurophysiological tools
  • Software architecture
  • Neurophysiological experiments

Cite this

Ioannou, C., Kindler, E., Bækgaard, P., Saqid, S., & Weber, B. (2019). Towards a software architecture for neurophysiological experiments. In Proceedings of the NeuroIS Retreat 2019 (pp. 155-163). Springer. https://doi.org/10.1007/978-3-030-28144-1_17