The non-invasive Berlin Brain-Computer Interface: fast acquisition of effective performance in untrained subjects.

doi:10.1016/J.NEUROIMAGE.2007.01.051

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The non-invasive Berlin Brain-Computer Interface: fast acquisition of effective performance in untrained subjects.

Journal Article•DOI•

The non-invasive Berlin Brain-Computer Interface: fast acquisition of effective performance in untrained subjects.

Benjamin Blankertz, Guido Dornhege, Matthias Krauledat¹, Klaus-Robert Müller¹, Gabriel Curio² - Show less +1 more•Institutions (2)

Technical University of Berlin¹, Charité²

15 Aug 2007-NeuroImage (Neuroimage)-Vol. 37, Iss: 2, pp 539-550

TL;DR: It is proposed that the key to quick efficiency in the BBCI system is its flexibility due to complex but physiologically meaningful features and its adaptivity which respects the enormous inter-subject variability.

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About: This article is published in NeuroImage.The article was published on 2007-08-15. It has received 865 citations till now. The article focuses on the topics: Brain–computer interface.

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Citations

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Journal Article•DOI•

Optimizing Spatial filters for Robust EEG Single-Trial Analysis

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Benjamin Blankertz, Ryota Tomioka¹, S. Lemm², Motoaki Kawanabe³, Klaus-Robert Müller⁴, Klaus-Robert Müller⁵ - Show less +2 more•Institutions (5)

University of Florida¹, Conexant², Fraunhofer Institute for Open Communication Systems³, Technical University of Berlin⁴, University of Potsdam⁵

01 Jan 2008-IEEE Signal Processing Magazine

TL;DR: The theoretical background of the common spatial pattern (CSP) algorithm, a popular method in brain-computer interface (BCD research), is elucidated and tricks of the trade for achieving a powerful CSP performance are revealed.

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Abstract: Due to the volume conduction multichannel electroencephalogram (EEG) recordings give a rather blurred image of brain activity. Therefore spatial filters are extremely useful in single-trial analysis in order to improve the signal-to-noise ratio. There are powerful methods from machine learning and signal processing that permit the optimization of spatio-temporal filters for each subject in a data dependent fashion beyond the fixed filters based on the sensor geometry, e.g., Laplacians. Here we elucidate the theoretical background of the common spatial pattern (CSP) algorithm, a popular method in brain-computer interface (BCD research. Apart from reviewing several variants of the basic algorithm, we reveal tricks of the trade for achieving a powerful CSP performance, briefly elaborate on theoretical aspects of CSP, and demonstrate the application of CSP-type preprocessing in our studies of the Berlin BCI (BBCI) project.

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1,799 citations

Cites background or methods from "The non-invasive Berlin Brain-Compu..."

...FOR SUBJECT AU NO REASONABLE CLASSIFIER COULD BE TRAINED (CROSS-VALIDATION ACCURACY BELOW 65% IN THE CALIBRATION DATA), SEE [2] FOR AN ANALYSIS OF THAT SPECIFIC CASE....
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...But there is report that on-line performance can be much enhanced by subject-specific settings [2]....
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...One of the reasons for this development is the striking advances of BCI systems with respect to usability, information transfer, and robustness for which modern machine learning and signal processing techniques have been instrumental [2], [4], [14], and [15]....
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...Details can be found in [2], [3], and [7]....
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...The next trial starts after 520 ms (see [2], [4], [7] for more details)....
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Journal Article•DOI•

Single-Trial Analysis and Classification of ERP Components - a Tutorial

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Benjamin Blankertz, Steven Lemm¹, Matthias S. Treder, Stefan Haufe, Klaus-Robert Müller - Show less +1 more•Institutions (1)

Analysis Group¹

15 May 2011-NeuroImage

TL;DR: This tutorial proposes to use shrinkage estimators and shows that appropriate regularization of linear discriminant analysis (LDA) by shrinkage yields excellent results for single-trial ERP classification that are far superior to classical LDA classification.

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1,046 citations

Cites background or methods from "The non-invasive Berlin Brain-Compu..."

...…extraction or classification, there is substantial variability in the classification accuracy both between subjects (Guger et al., 2003, 2009; Blankertz et al., 2007a; Krauledat et al., 2008; Dickhaus et al., 2009; Allison et al., 2009) and within subjects during the course of an experiment…...
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...…Kübler et al., 2001), which finally also attracted the machine learning community to the field (Blankertz et al., 2002; Vidaurre and Blankertz, 2010; Hong et al., 2009; Müller et al., 2008; Blankertz et al., 2008b, 2007a, 2006; Parra et al., 2008, 2003; Wang et al., 2004; Tomioka and Müller, 2010)....
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...The present data is based on a novel variant of Hex-o-Spell (Treder and Blankertz, 2010), a mental typewriter which was originally controlled by means of motor imagery (Williamson et al., 2009; Blankertz et al., 2007b; Müller and Blankertz, 2006) while the new variant is based on ERPs....
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...However, regardless of the particular techniques employed for feature extraction or classification, there is substantial variability in the classification accuracy both between subjects [26, 7, 30, 25, 17, 1] and within subjects during the course of an experiment [62]....
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...This branch of research is strongly influenced by the development of an effective communication interface connecting the human brain and a computer ([19, 33, 35, 76, 5, 52, 16, 77, 34]), which finally also attracted the machine learning community to the field [6, 69, 27, 47, 11, 50, 7, 8, 74, 49, 67]....
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Proceedings Article•DOI•

Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface

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Kai Keng Ang¹, Zhang Yang Chin¹, Haihong Zhang¹, Cuntai Guan¹•Institutions (1)

Agency for Science, Technology and Research¹

01 Jun 2008

TL;DR: A novel filter bank common spatial pattern (FBCSP) is proposed to perform autonomous selection of key temporal-spatial discriminative EEG characteristics and shows that FBCSP, using a particular combination feature selection and classification algorithm, yields relatively higher cross-validation accuracies compared to prevailing approaches.

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Abstract: In motor imagery-based brain computer interfaces (BCI), discriminative patterns can be extracted from the electroencephalogram (EEG) using the common spatial pattern (CSP) algorithm. However, the performance of this spatial filter depends on the operational frequency band of the EEG. Thus, setting a broad frequency range, or manually selecting a subject-specific frequency range, are commonly used with the CSP algorithm. To address this problem, this paper proposes a novel filter bank common spatial pattern (FBCSP) to perform autonomous selection of key temporal-spatial discriminative EEG characteristics. After the EEG measurements have been bandpass-filtered into multiple frequency bands, CSP features are extracted from each of these bands. A feature selection algorithm is then used to automatically select discriminative pairs of frequency bands and corresponding CSP features. A classification algorithm is subsequently used to classify the CSP features. A study is conducted to assess the performance of a selection of feature selection and classification algorithms for use with the FBCSP. Extensive experimental results are presented on a publicly available dataset as well as data collected from healthy subjects and unilaterally paralyzed stroke patients. The results show that FBCSP, using a particular combination feature selection and classification algorithm, yields relatively higher cross-validation accuracies compared to prevailing approaches.

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991 citations

Cites methods from "The non-invasive Berlin Brain-Compu..."

...The following feature selection algorithms are used in this paper:...
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Journal Article•DOI•

Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b.

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Kai Keng Ang¹, Zheng Yang Chin¹, Chuanchu Wang¹, Cuntai Guan¹, Haihong Zhang¹ - Show less +1 more•Institutions (1)

Institute for Infocomm Research Singapore¹

29 Mar 2012-Frontiers in Neuroscience

TL;DR: The FBCSP algorithm performed relatively the best among the other submitted algorithms and yielded a mean kappa value of 0.569 and 0.600 across all subjects in Datasets 2a and 2b of the BCI Competition IV.

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Abstract: The Common Spatial Pattern (CSP) algorithm is an effective and popular method for classifying 2-class motor imagery electroencephalogram (EEG) data, but its effectiveness depends on the subject-specific frequency band. This paper presents the Filter Bank Common Spatial Pattern (FBCSP) algorithm to optimize the subject-specific frequency band for CSP on Datasets 2a and 2b of the Brain-Computer Interface (BCI) Competition IV. Dataset 2a comprised 4 classes of 22 channels EEG data from 9 subjects, and Dataset 2b comprised 2 classes of 3 bipolar channels EEG data from 9 subjects. Multi-class extensions to FBCSP are also presented to handle the 4-class EEG data in Dataset 2a, namely, Divide-and-Conquer (DC), Pair-Wise (PW), and One-Versus-Rest (OVR) approaches. Two feature selection algorithms are also presented to select discriminative CSP features on Dataset 2b, namely, the Mutual Information-based Best Individual Feature (MIBIF) algorithm, and the Mutual Information-based Rough Set Reduction (MIRSR) algorithm. The single-trial classification accuracies were presented using 10x10-fold cross-validations on the training data and session-to-session transfer on the evaluation data from both datasets. Disclosure of the test data labels after the BCI Competition IV showed that the FBCSP algorithm performed relatively the best among the other submitted algorithms and yielded a mean kappa value of 0.569 and 0.600 across all subjects in Datasets 2a and 2b respectively.

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862 citations

Cites background from "The non-invasive Berlin Brain-Compu..."

...INTRODUCTION The challenge in Motor Imagery-based BCI (MI-BCI), which translates the mental imagination of movement to commands, is the huge inter-subject variability with respect to the characteristics of the brain signals (Blankertz et al., 2007)....
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...However, the performance of the CSP algorithm can be potentially enhanced by subjectspecific parameters (Blankertz et al., 2007)....
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...The challenge in Motor Imagery-based BCI (MI-BCI), which translates the mental imagination of movement to commands, is the huge inter-subject variability with respect to the characteristics of the brain signals (Blankertz et al., 2007)....
[...]

Journal Article•

Covariate Shift Adaptation by Importance Weighted Cross Validation

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Masashi Sugiyama¹, Matthias Krauledat², Klaus-Robert Müller²•Institutions (2)

Tokyo Institute of Technology¹, Technical University of Berlin²

01 Dec 2007-Journal of Machine Learning Research

TL;DR: This paper proposes a new method called importance weighted cross validation (IWCV), for which its unbiasedness even under the covariate shift is proved, and the IWCV procedure is the only one that can be applied for unbiased classification under covariates.

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Abstract: A common assumption in supervised learning is that the input points in the training set follow the same probability distribution as the input points that will be given in the future test phase However, this assumption is not satisfied, for example, when the outside of the training region is extrapolated The situation where the training input points and test input points follow different distributions while the conditional distribution of output values given input points is unchanged is called the covariate shift Under the covariate shift, standard model selection techniques such as cross validation do not work as desired since its unbiasedness is no longer maintained In this paper, we propose a new method called importance weighted cross validation (IWCV), for which we prove its unbiasedness even under the covariate shift The IWCV procedure is the only one that can be applied for unbiased classification under covariate shift, whereas alternatives to IWCV exist for regression The usefulness of our proposed method is illustrated by simulations, and furthermore demonstrated in the brain-computer interface, where strong non-stationarity effects can be seen between training and test sessions

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807 citations

Cites methods from "The non-invasive Berlin Brain-Compu..."

...The experimental setting is described in more detail in the references (Blankertz et al., 2007, 2006; Sugiyama et al., 2006)....
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References

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Book•

Adaptive Filter Theory

[...]

Simon Haykin

01 Jan 1986

TL;DR: In this paper, the authors propose a recursive least square adaptive filter (RLF) based on the Kalman filter, which is used as the unifying base for RLS Filters.

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Abstract: Background and Overview. 1. Stochastic Processes and Models. 2. Wiener Filters. 3. Linear Prediction. 4. Method of Steepest Descent. 5. Least-Mean-Square Adaptive Filters. 6. Normalized Least-Mean-Square Adaptive Filters. 7. Transform-Domain and Sub-Band Adaptive Filters. 8. Method of Least Squares. 9. Recursive Least-Square Adaptive Filters. 10. Kalman Filters as the Unifying Bases for RLS Filters. 11. Square-Root Adaptive Filters. 12. Order-Recursive Adaptive Filters. 13. Finite-Precision Effects. 14. Tracking of Time-Varying Systems. 15. Adaptive Filters Using Infinite-Duration Impulse Response Structures. 16. Blind Deconvolution. 17. Back-Propagation Learning. Epilogue. Appendix A. Complex Variables. Appendix B. Differentiation with Respect to a Vector. Appendix C. Method of Lagrange Multipliers. Appendix D. Estimation Theory. Appendix E. Eigenanalysis. Appendix F. Rotations and Reflections. Appendix G. Complex Wishart Distribution. Glossary. Abbreviations. Principal Symbols. Bibliography. Index.

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16,062 citations

Book•

Introduction to Statistical Pattern Recognition

[...]

Keinosuke Fukunaga

01 Jan 1972

TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.

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Abstract: This completely revised second edition presents an introduction to statistical pattern recognition Pattern recognition in general covers a wide range of problems: it is applied to engineering problems, such as character readers and wave form analysis as well as to brain modeling in biology and psychology Statistical decision and estimation, which are the main subjects of this book, are regarded as fundamental to the study of pattern recognition This book is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field Each chapter contains computer projects as well as exercises

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10,526 citations

Journal Article•DOI•

Brain-computer interfaces for communication and control.

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Jonathan R. Wolpaw¹, Jonathan R. Wolpaw², Niels Birbaumer³, Niels Birbaumer⁴, Dennis J. McFarland¹, Gert Pfurtscheller⁵, Theresa M. Vaughan¹ - Show less +3 more•Institutions (5)

New York State Department of Health¹, State University of New York System², University of Padua³, University of Tübingen⁴, University of Graz⁵

01 Jun 2002-Clinical Neurophysiology

TL;DR: With adequate recognition and effective engagement of all issues, BCI systems could eventually provide an important new communication and control option for those with motor disabilities and might also give those without disabilities a supplementary control channel or a control channel useful in special circumstances.

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6,803 citations

Journal Article•DOI•

[...]

Gert Pfurtscheller¹, F.H. Lopes da Silva•Institutions (1)

University of Graz¹

01 Nov 1999-Clinical Neurophysiology

TL;DR: Quantification of ERD/ERS in time and space is demonstrated on data from a number of movement experiments, whereby either the same or different locations on the scalp can display ERD and ERS simultaneously.

...read moreread less

6,093 citations

"The non-invasive Berlin Brain-Compu..." refers methods in this paper

...For the motor imagery conditions we essentially expected two effects: regularly, an ERD over the sensorimotor area corresponding to the limb for which motor imagery was performed ([Pfurtscheller and Lopes da Silva, 1999]), and, potentially, an ERS over flanking sensorimotor areas, possibly reflecting an ‘surround inhibition’ enhancing focal cortical activation, see [Neuper and Pfurtscheller, 2001], [Pfurtscheller et al....
[...]
...This attenuation of brain rhythms is termed event-related desynchronization (ERD), see [Pfurtscheller and Lopes da Silva, 1999], [Pfurtscheller et al....
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Book•

Introduction to statistical pattern recognition (2nd ed.)

[...]

Keinosuke Fukunaga¹•Institutions (1)

Purdue University¹

01 Sep 1990

4,384 citations

"The non-invasive Berlin Brain-Compu..." refers background or methods in this paper

...For the technical details the reader is referred to Fukunaga (1990), Ramoser et al. (2000), and Lemm et al. (2005)....
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...Common spatial pattern (CSP) analysis The common spatial pattern (CSP) algorithm (Fukunaga, 1990) is highly successful in calculating spatial filters for detecting ERD/ ERS effects (see Koles and Soong, 1998) and for ERD-based BCIs (see Guger et al., 2000b) and has been extended to multi-class…...
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