pySPACE - a signal processing and classification environment in Python

Mario Michael Krell, Sirko Straube, Anett Seeland, Hendrik Wöhrle, Johannes Teiwes, Jan Hendrik Metzen, Elsa Andrea Kirchner, Frank Kirchner
In Frontiers in Neuroinformatics, frontiers, volume 7, number 40, pages 1-11, Dec/2013.

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.

Keywords

Python, neuroscience, EEG, YAML, benchmarking, signal processing, machine learning, visualization

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@article{Krell2013PySPACE,
  author    = {Krell, Mario Michael and Straube, Sirko and Seeland, Anett and Wöhrle, Hendrik and
               Teiwes, Johannes and Metzen, Jan Hendrik and Kirchner, Elsa Andrea and Kirchner,
               Frank},
  title     = {{pySPACE} - a signal processing and classification environment in Python},
  journal   = {Frontiers in Neuroinformatics},
  volume    = {7},
  number    = {40},
  pages     = {1--11},
  month     = dec,
  year      = {2013},
  publisher = {frontiers},
  url       = {https://robotik.dfki-bremen.de/en/research/publications/7148}
}
Download (.bib) 
TY  - JOUR
AU  - Krell, Mario Michael
AU  - Straube, Sirko
AU  - Seeland, Anett
AU  - Wöhrle, Hendrik
AU  - Teiwes, Johannes
AU  - Metzen, Jan Hendrik
AU  - Kirchner, Elsa Andrea
AU  - Kirchner, Frank
TI  - pySPACE - a signal processing and classification environment in Python
T2  - Frontiers in Neuroinformatics
VL  - 7
IS  - 40
SP  - 1
EP  - 11
PY  - 2013
DA  - 2013/12//
PB  - frontiers
AB  - 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.
KW  - Python
KW  - neuroscience
KW  - EEG
KW  - YAML
KW  - benchmarking
KW  - signal processing
KW  - machine learning
KW  - visualization
UR  - https://robotik.dfki-bremen.de/en/research/publications/7148
LA  - eng
ER  -
Download (.ris) 
Krell, M. M., Straube, S., Seeland, A., Wöhrle, H., Teiwes, J., Metzen, J. H., Kirchner, E. A., & Kirchner, F. (2013). pySPACE - a signal processing and classification environment in Python. Frontiers in Neuroinformatics, 7(40), 1–11. https://robotik.dfki-bremen.de/en/research/publications/7148


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