BCI competition 2003-data set IIb: support vector machines for the P300 speller paradigm
24 May 2004-IEEE Transactions on Biomedical Engineering (IEEE Trans Biomed Eng)-Vol. 51, Iss: 6, pp 1073-1076
TL;DR: An approach to analyze data from the P300 speller paradigm using the machine-learning technique support vector machines, which is fast, requires only 10 electrode positions and demands only a small amount of preprocessing.
Abstract: We propose an approach to analyze data from the P300 speller paradigm using the machine-learning technique support vector machines. In a conservative classification scheme, we found the correct solution after five repetitions. While the classification within the competition is designed for offline analysis, our approach is also well-suited for a real-world online solution: It is fast, requires only 10 electrode positions and demands only a small amount of preprocessing.
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TL;DR: This paper compares classification algorithms used to design brain-computer interface (BCI) systems based on electroencephalography (EEG) in terms of performance and provides guidelines to choose the suitable classification algorithm(s) for a specific BCI.
Abstract: In this paper we review classification algorithms used to design brain–computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.
2,519 citations
Cites background or methods from "BCI competition 2003-data set IIb: ..."
...A great variety of features have been attempted to design BCI such as amplitude values of EEG signals [10], band powers (BP) [11], power spectral density (PSD) values [12, 13], autoregressive (AR) and adaptive autoregressive (AAR) parameters [8, 14], timefrequency features [15] and inverse model-based features [16–18]....
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...RBF SVM have also given very good results for BCI applications [10, 35]....
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TL;DR: The state-of-the-art of BCIs are reviewed, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface.
Abstract: A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.
1,407 citations
Cites methods from "BCI competition 2003-data set IIb: ..."
...The Gaussian SVM has been applied in BCIs to classify P300 evoked potentials [241–243]....
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TL;DR: This work presents the first such comprehensive survey of all BCI designs using electrical signal recordings published prior to January 2006, and asks what are the key signal processing components of a BCI, and what signal processing algorithms have been used in BCIs.
Abstract: Brain–computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using the electroencephalographic activity or other electrophysiological measures of the brain function. An essential factor in the successful operation of BCI systems is the methods used to process the brain signals. In the BCI literature, however, there is no comprehensive review of the signal processing techniques used. This work presents the first such comprehensive survey of all BCI designs using electrical signal recordings published prior to January 2006. Detailed results from this survey are presented and discussed. The following key research questions are addressed: (1) what are the key signal processing components of a BCI, (2) what signal processing algorithms have been used in BCIs and (3) which signal processing techniques have received more attention? S This article has associated online supplementary data files
844 citations
Cites background from "BCI competition 2003-data set IIb: ..."
...…2003c, Garrett et al 2003, Glassman 2005, Gysels and Celka 2004, Gysels et al 2005, Hung et al 2005, Guan et al 2005, Kaper and Ritter 2004a, 2004b, Kaper et al 2004, Lal et al 2004, Peterson et al 2005, Schroder et al 2003, Schröder et al 2005, Thulasidas et al 2004, Trejo et al 2003, Xu et al…...
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TL;DR: The results indicate that while all methods attained acceptable performance levels, SWLDA and FLD provide the best overall performance and implementation characteristics for practical classification of P300 Speller data.
Abstract: This study assesses the relative performance characteristics of five established classification techniques on data collected using the P300 Speller paradigm, originally described by Farwell and Donchin (1988 Electroenceph. Clin. Neurophysiol. 70 510). Four linear methods: Pearson's correlation method (PCM), Fisher's linear discriminant (FLD), stepwise linear discriminant analysis (SWLDA) and a linear support vector machine (LSVM); and one nonlinear method: Gaussian kernel support vector machine (GSVM), are compared for classifying offline data from eight users. The relative performance of the classifiers is evaluated, along with the practical concerns regarding the implementation of the respective methods. The results indicate that while all methods attained acceptable performance levels, SWLDA and FLD provide the best overall performance and implementation characteristics for practical classification of P300 Speller data.
759 citations
Cites methods from "BCI competition 2003-data set IIb: ..."
...Several classification techniques have demonstrated notable performance for the P300 Speller, including stepwise linear discriminant analysis [2, 5], support vector machines [8, 10, 11], wavelets [1] and matched filtering [14]....
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University of Freiburg1, University of Tübingen2, University of Trento3, University of California, San Diego4, Graz University of Technology5, École Polytechnique Fédérale de Lausanne6, Imperial College London7, University of Washington8, University of Hamburg9, University of Arkansas for Medical Sciences10, Institute of Science and Technology Austria11, Technical University of Berlin12, University College London13
TL;DR: 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 and it is the hope that winning entries may enhance the analysis methods of future BCIs.
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) session-to-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.
747 citations
Cites methods from "BCI competition 2003-data set IIb: ..."
...This worked very well in the BCI competition II where all winner articles have been published in volume 51 of IEEE Trans Biomed Eng (Blanchard and Blankertz, 2004; Bostanov, 2004; Kaper et al., 2004; Lemm et al., 2004; Mensh et al., 2004; Wang et al., 2004; Xu et al., 2004)....
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References
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01 Jan 1995TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Abstract: Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.
40,147 citations
"BCI competition 2003-data set IIb: ..." refers background in this paper
...To describe this optimal hyperplane, only the vectors on the margin, the so-called support vectors, are necessary [5]....
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TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Abstract: The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global. We describe how support vector training can be practically implemented, and discuss in detail the kernel mapping technique which is used to construct SVM solutions which are nonlinear in the data. We show how Support Vector machines can have very large (even infinite) VC dimension by computing the VC dimension for homogeneous polynomial and Gaussian radial basis function kernels. While very high VC dimension would normally bode ill for generalization performance, and while at present there exists no theory which shows that good generalization performance is guaranteed for SVMs, there are several arguments which support the observed high accuracy of SVMs, which we review. Results of some experiments which were inspired by these arguments are also presented. We give numerous examples and proofs of most of the key theorems. There is new material, and I hope that the reader will find that even old material is cast in a fresh light.
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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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01 Jan 1996TL;DR: Professor Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural networks in this self-contained account.
Abstract: From the Publisher:
Pattern recognition has long been studied in relation to many different (and mainly unrelated) applications, such as remote sensing, computer vision, space research, and medical imaging. In this book Professor Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural networks. Unifying principles are brought to the fore, and the author gives an overview of the state of the subject. Many examples are included to illustrate real problems in pattern recognition and how to overcome them.This is a self-contained account, ideal both as an introduction for non-specialists readers, and also as a handbook for the more expert reader.
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TL;DR: This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods.
Abstract: This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods. We first give a short background about Vapnik-Chervonenkis theory and kernel feature spaces and then proceed to kernel based learning in supervised and unsupervised scenarios including practical and algorithmic considerations. We illustrate the usefulness of kernel algorithms by discussing applications such as optical character recognition and DNA analysis.
3,566 citations
"BCI competition 2003-data set IIb: ..." refers methods in this paper
...Index Terms—BCI competition 2003, brain-computer interface, P300 speller, SVM. I. INTRODUCTION SUPPORT VECTOR Machines (SVM) have been success-fully applied for classification and regression in various domains of pattern recognition [1]....
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