title: pySPACE-a signal processing and classification environment in Python.
abstract: In neuroscience large amounts of data are recorded to provide insights into cerebral information processing and function. The successful extraction of the relevant signals becomes more and more challenging due to increasing complexities in acquisition techniques and questions addressed. Here, automated signal processing and machine learning tools can help to process the data, e.g., to separate signal and noise. With the presented software pySPACE (http://pyspace.github.io/pyspace), signal processing algorithms can be compared and applied automatically on time series data, either with the aim of finding a suitable preprocessing, or of training supervised algorithms to classify the data. pySPACE originally has been built to process multi-sensor windowed time series data, like event-related potentials from the electroencephalogram (EEG). The software provides automated data handling, distributed processing, modular build-up of signal processing chains and tools for visualization and performance evaluation. Included in the software are various algorithms like temporal and spatial filters, feature generation and selection, classification algorithms, and evaluation schemes. Further, interfaces to other signal processing tools are provided and, since pySPACE is a modular framework, it can be extended with new algorithms according to individual needs. In the presented work, the structural hierarchies are described. It is illustrated how users and developers can interface the software and execute offline and online modes. Configuration of pySPACE is realized with the YAML format, so that programming skills are not mandatory for usage. The concept of pySPACE is to have one comprehensive tool that can be used to perform complete signal processing and classification tasks. It further allows to define own algorithms, or to integrate and use already existing libraries.
authors.affiliation: Robotics Group, Faculty 3 - Mathematics and Computer Science, University of Bremen Bremen, Germany.
authors.forename: Mario M
authors.initials: MM
authors.lastname: Krell
authors.affiliation: Robotics Group, Faculty 3 - Mathematics and Computer Science, University of Bremen Bremen, Germany.
authors.forename: Sirko
authors.initials: S
authors.lastname: Straube
authors.affiliation: Robotics Innovation Center, DFKI GmbH Bremen, Germany.
authors.forename: Anett
authors.initials: A
authors.lastname: Seeland
authors.affiliation: Robotics Innovation Center, DFKI GmbH Bremen, Germany.
authors.forename: Hendrik
authors.initials: H
authors.lastname: Wöhrle
authors.affiliation: Robotics Group, Faculty 3 - Mathematics and Computer Science, University of Bremen Bremen, Germany.
authors.forename: Johannes
authors.initials: J
authors.lastname: Teiwes
authors.affiliation: Robotics Group, Faculty 3 - Mathematics and Computer Science, University of Bremen Bremen, Germany.
authors.forename: Jan H
authors.initials: JH
authors.lastname: Metzen
authors.affiliation: Robotics Group, Faculty 3 - Mathematics and Computer Science, University of Bremen Bremen, Germany ; Robotics Innovation Center, DFKI GmbH Bremen, Germany.
authors.forename: Elsa A
authors.initials: EA
authors.lastname: Kirchner
authors.affiliation: Robotics Group, Faculty 3 - Mathematics and Computer Science, University of Bremen Bremen, Germany ; Robotics Innovation Center, DFKI GmbH Bremen, Germany.
authors.forename: Frank
authors.initials: F
authors.lastname: Kirchner
keywords: EEG; Python; YAML; benchmarking; machine learning; neuroscience; signal processing; visualization