Authors:
Roman Mouček
and
Filip Kupilík
Affiliation:
Department of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia, Univerzitní 8, Plzeň, Czech Republic
Keyword(s):
Deep Learning, EEG Data Standards, EEG Workflows, EEG Pipelines, Electroencephalography, Event-related Potentials, Human Brain, Machine Learning, Reproducibility.
Abstract:
With increasing amounts of experimental data, openness, fairness, and reproducibility of scientific experimental work have become important factors for researchers, journals and funding bodies. However, these kinds of challenges are not easily and directly achievable. The goal of this paper is to contribute to these efforts by introducing advances in building more mature lifecycle of electroencephalography/event-related potential data. The progressive data standardization initiatives, data formats, and trends in using machine and deep learning methods for processing of domain data are described and discussed. An open processing workflow based on the analysis of current software tools for preprocessing, processing and classification of electroencephalography/event-related potential data is proposed, implemented and verified on a publicly available dataset.