Adaptive Methods in BCI Research - An Introductory Tutorial

Alois Schlögl; Carmen Vidaurre; Klaus Robert Müller

doi:10.1007/978-3-642-02091-9_18

Adaptive Methods in BCI Research - An Introductory Tutorial

Alois Schlögl^*
, Carmen Vidaurre
, Klaus Robert Müller

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

26 Citations (Scopus)

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.

Original language	English
Title of host publication	Frontiers Collection
Publisher	Springer VS
Pages	331-355
Number of pages	25
DOIs	https://doi.org/10.1007/978-3-642-02091-9_18
Publication status	Published - 2009
Externally published	Yes

Publication series

Name	Frontiers Collection
Volume	Part F952
ISSN (Print)	1612-3018
ISSN (Electronic)	2197-6619

Keywords

Adaptive Estimator
Kalman Filter
Linear Discriminant Analysis
Recursive Little Square
Recursive Little Square Algorithm

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10.1007/978-3-642-02091-9_18

Cite this

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title = "Adaptive Methods in BCI Research - An Introductory Tutorial",

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.",

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doi = "10.1007/978-3-642-02091-9\_18",

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series = "Frontiers Collection",

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AU - Vidaurre, Carmen

AU - Müller, Klaus Robert

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AB - 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.

KW - Adaptive Estimator

KW - Kalman Filter

KW - Linear Discriminant Analysis

KW - Recursive Little Square

KW - Recursive Little Square Algorithm

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BT - Frontiers Collection

PB - Springer VS

ER -

Adaptive Methods in BCI Research - An Introductory Tutorial

Abstract

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Keywords

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