BioSig: The Free and Open Source Software Library for Biomedical Signal Processing

BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open source software tools for many different application areas. Some of the areas where BioSig can be employed are ne...

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Bibliographic Details
Published in:Computational Intelligence and Neuroscience 2011-01, Vol.2011 (2011), p.393-404
Main Authors: Vidaurre, Carmen, Sander, Tilmann H., Schlögl, Alois
Format: Article
Language:English
Subjects:
Algorithms
Biomedical engineering
Brain - physiology
C plus plus
Computer science
Computer Simulation
Data processing
Digital signal processors
Electrocardiogram
Electrocardiography
Electroencephalography
Electromyography
Electrooculography
Heart Rate - physiology
Humans
Libraries
Methods
Models, Neurological
Neurophysiology
Personal computers
Proprietary
Public software
Quality control
Reading
Regression analysis
Signal processing
Signal Processing, Computer-Assisted
Software
Stochastic models
Studies
Technological change
Technology application
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Summary:BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open source software tools for many different application areas. Some of the areas where BioSig can be employed are neuroinformatics, brain-computer interfaces, neurophysiology, psychology, cardiovascular systems, and sleep research. Moreover, the analysis of biosignals such as the electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), or respiration signals is a very relevant element of the BioSig project. Specifically, BioSig provides solutions for data acquisition, artifact processing, quality control, feature extraction, classification, modeling, and data visualization, to name a few. In this paper, we highlight several methods to help students and researchers to work more efficiently with biomedical signals.
ISSN:1687-5265
1687-5273
DOI:10.1155/2011/935364