Cloud Infrastructure for Storing and Processing EEG and ERP Experimental Data

Current infrastructures for experimental data, results and computational tools make a shift from locally maintained solutions to remote cloud-based infrastructures. It brings a higher availability, sustainability and performance. However, specifics of different research areas require development of customized solutions for individual research domains. For example, electroencephalography and event-related potentials (EEG/ERP) use specific devices, data formats and machine learning workflows. As a solution, a cloud-based system for the EEG/ERP domain containing a distributed data storage, a signal processing method library and a client GUI is presented. The signal processing method library is used for training of classifiers and classifying the data in the cloud-based system controlled by the GUI. The presented system was tested using a machine learning workflow based on the data stored in the system. In the workflow, various classifiers were trained and their parameters stored into the system. Finally, testing data were classified using previously trained classifiers.