Automation of EEG Data Processing with HPC Community Cloud

We study and further develop an approach based on the computational models formalism to automation of data processing pipelines in a specific application domain-neurophysiology. A computational model is a formal specification of a set of variables (in a mathematical sense) of an application domain and a set of operations with their input and output variables. If values of some variables are given, the operations that have these variables as inputs can compute values of their output variables, these variables serve as inputs for other operations and, thus, some subset of variables can obtain their values. This way, all possible/meaningful computational scenarios in the domain can be defined and algorithms (scenarios) for particular computational problems can be automatically derived by forward chaining. In this work, we apply the approach to building a framework for managing data and computations in neurophysiology. Brain studies produce large amounts of data such as electroencephalograms, functional magnetic tomography images, questionnaires, and results of other medical tests. There is a need for systematic management of these data, specification of data processing pipelines, implementing these pipelines on high performance computing systems, representing the process of computing and the results with a graphic user interface. We build a sample computational model to represent a limited set of scenarios that can be reused and further extended in a regular way by adding more variables and operations, propose a framework for management of data and computations, automation of building graphical user interfaces. A prototype implementation of the framework based on the developed computation model is a practical output of this work.