@article{493,
  abstract     = {The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community. As experienced already in prior competitions not only scientists from the narrow field of BCI compete, but scholars with a broad variety of backgrounds and nationalities. They include high specialists as well as students.The goals of all BCI competitions have always been to challenge with respect to novel paradigms and complex data. We report on the following challenges: (1) asynchronous data, (2) synthetic, (3) multi-class continuous data, (4) sessionto-session transfer, (5) directionally modulated MEG, (6) finger movements recorded by ECoG. As after past competitions, our hope is that winning entries may enhance the analysis methods of future BCIs.},
  author       = {Tangermann, Michael and Müller, Klaus and Aertsen, Ad and Birbaumer, Niels and Braun, Christoph and Brunner, Clemens and Leeb, Robert and Mehring, Carsten and Miller, Kai and Müller Putz, Gernot and Nolte, Guido and Pfurtscheller, Gert and Preissl, Hubert and Schalk, Gerwin and Schlögl, Alois and Vidaurre, Carmen and Waldert, Stephan and Blankertz, Benjamin},
  journal      = {Frontiers in Neuroscience},
  publisher    = {Frontiers Research Foundation},
  title        = {{Review of the BCI competition IV}},
  doi          = {10.3389/fnins.2012.00055},
  volume       = {6},
  year         = {2012},
}

@article{490,
  abstract     = {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. },
  author       = {Schlögl, Alois and Vidaurre, Carmen and Sander, Tilmann},
  journal      = {Computational Intelligence and Neuroscience},
  publisher    = {Hindawi Publishing Corporation},
  title        = {{BioSig: The free and open source software library for biomedical signal processing}},
  doi          = {10.1155/2011/935364},
  volume       = {2011},
  year         = {2011},
}

@inbook{14983,
  abstract     = {This chapter tackles a difficult challenge: presenting signal processing material to non-experts. This chapter is meant to be comprehensible to people who have some math background, including a course in linear algebra and basic statistics, but do not specialize in mathematics, engineering, or related fields. Some formulas assume the reader is familiar with matrices and basic matrix operations, but not more advanced material. Furthermore, we tried to make the chapter readable even if you skip the formulas. Nevertheless, we include some simple methods to demonstrate the basics of adaptive data processing, then we proceed with some advanced methods that are fundamental in adaptive signal processing, and are likely to be useful in a variety of applications. The advanced algorithms are also online available [30]. In the second part, these techniques are applied to some real-world BCI data.},
  author       = {Schlögl, Alois and Vidaurre, Carmen and Müller, Klaus-Robert},
  booktitle    = {Brain-Computer Interfaces},
  editor       = {Graimann, Bernhard and Pfurtscheller, Gert and Allison, Brendan},
  isbn         = {9783642020902},
  issn         = {1612-3018},
  pages        = {331--355},
  publisher    = {Springer},
  title        = {{Adaptive Methods in BCI Research - An Introductory Tutorial}},
  doi          = {10.1007/978-3-642-02091-9_18},
  year         = {2010},
}