pySPACE - A Signal Processing and Classification Environment in Python
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, automa...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2013-12-01
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| Series: | Frontiers in Neuroinformatics |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fninf.2013.00040/full |
| _version_ | 1818320678652215296 |
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| author | Mario Michael Krell Sirko eStraube Anett eSeeland Hendrik eWöhrle Johannes eTeiwes Jan Hendrik Metzen Elsa Andrea Kirchner Elsa Andrea Kirchner Frank eKirchner Frank eKirchner |
| author_facet | Mario Michael Krell Sirko eStraube Anett eSeeland Hendrik eWöhrle Johannes eTeiwes Jan Hendrik Metzen Elsa Andrea Kirchner Elsa Andrea Kirchner Frank eKirchner Frank eKirchner |
| author_sort | Mario Michael Krell |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T10:28:50Z |
| format | Article |
| id | doaj.art-0ffdd9f039254bf68f1a2798ee780a99 |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-12-13T10:28:50Z |
| publishDate | 2013-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-0ffdd9f039254bf68f1a2798ee780a992022-12-21T23:50:55ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962013-12-01710.3389/fninf.2013.0004065191pySPACE - A Signal Processing and Classification Environment in PythonMario Michael Krell0Sirko eStraube1Anett eSeeland2Hendrik eWöhrle3Johannes eTeiwes4Jan Hendrik Metzen5Elsa Andrea Kirchner6Elsa Andrea Kirchner7Frank eKirchner8Frank eKirchner9University of BremenUniversity of BremenDFKI GmbHDFKI GmbHUniversity of BremenUniversity of BremenUniversity of BremenDFKI GmbHUniversity of BremenDFKI GmbHIn 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.http://journal.frontiersin.org/Journal/10.3389/fninf.2013.00040/fullBenchmarkingEEGmachine learningNeurosciencevisualizationpython |
| spellingShingle | Mario Michael Krell Sirko eStraube Anett eSeeland Hendrik eWöhrle Johannes eTeiwes Jan Hendrik Metzen Elsa Andrea Kirchner Elsa Andrea Kirchner Frank eKirchner Frank eKirchner pySPACE - A Signal Processing and Classification Environment in Python Frontiers in Neuroinformatics Benchmarking EEG machine learning Neuroscience visualization python |
| title | pySPACE - A Signal Processing and Classification Environment in Python |
| title_full | pySPACE - A Signal Processing and Classification Environment in Python |
| title_fullStr | pySPACE - A Signal Processing and Classification Environment in Python |
| title_full_unstemmed | pySPACE - A Signal Processing and Classification Environment in Python |
| title_short | pySPACE - A Signal Processing and Classification Environment in Python |
| title_sort | pyspace a signal processing and classification environment in python |
| topic | Benchmarking EEG machine learning Neuroscience visualization python |
| url | http://journal.frontiersin.org/Journal/10.3389/fninf.2013.00040/full |
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