Journal ArticleDOI
Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting
Haiping Lu,How-Lung Eng,Cuntai Guan,Konstantinos N. Plataniotis,Anastasios N. Venetsanopoulos +4 more
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TLDR
A regularized CSP (R-CSP) algorithm is proposed, where the covariance-matrix estimation is regularized by two parameters to lower the estimation variance while reducing the estimation bias.Abstract:
Common spatial pattern (CSP) is a popular algorithm for classifying electroencephalogram (EEG) signals in the context of brain-computer interfaces (BCIs). This paper presents a regularization and aggregation technique for CSP in a small-sample setting (SSS). Conventional CSP is based on a sample-based covariance-matrix estimation. Hence, its performance in EEG classification deteriorates if the number of training samples is small. To address this concern, a regularized CSP (R-CSP) algorithm is proposed, where the covariance-matrix estimation is regularized by two parameters to lower the estimation variance while reducing the estimation bias. To tackle the problem of regularization parameter determination, R-CSP with aggregation (R-CSP-A) is further proposed, where a number of R-CSPs are aggregated to give an ensemble-based solution. The proposed algorithm is evaluated on data set IVa of BCI Competition III against four other competing algorithms. Experiments show that R-CSP-A significantly outperforms the other methods in average classification performance in three sets of experiments across various testing scenarios, with particular superiority in SSS.read more
Citations
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Journal ArticleDOI
Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system
Jasmin Kevric,Abdulhamit Subasi +1 more
TL;DR: Results indicate that the proposed model has the potential to obtain a reliable classification of motor imagery EEG signals, and can thus be used as a practical system for controlling a wheelchair.
Journal ArticleDOI
Temporally Constrained Sparse Group Spatial Patterns for Motor Imagery BCI
TL;DR: A novel algorithm, namely temporally constrained sparse group spatial pattern (TSGSP), is proposed for the simultaneous optimization of filter bands and time window within CSP to further boost classification accuracy of MI EEG.
Journal ArticleDOI
Sparse Bayesian Classification of EEG for Brain–Computer Interface
TL;DR: A sparse Bayesian method is introduced by exploiting Laplace priors, namely, SBLaplace, for EEG classification by learning a sparse discriminant vector with a Laplace prior in a hierarchical fashion under a Bayesian evidence framework.
Journal ArticleDOI
A Novel Bayesian Framework for Discriminative Feature Extraction in Brain-Computer Interfaces
Heung-Il Suk,Seong-Whan Lee +1 more
TL;DR: A novel Bayesian framework for discriminative feature extraction for motor imagery classification in an EEG-based BCI in which the class-discriminative frequency bands and the corresponding spatial filters are optimized by means of the probabilistic and information-theoretic approaches is proposed.
Journal ArticleDOI
Correlation-based channel selection and regularized feature optimization for MI-based BCI
TL;DR: A correlation-based channel selection (CCS) method is proposed to select the channels that contained more correlated information in this study to improve the classification performance of MI-based BCIs and a novel regularized common spatial pattern (RCSP) method was used to extract effective features.
References
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TL;DR: A common theoretical framework for combining classifiers which use distinct pattern representations is developed and it is shown that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision.
Journal ArticleDOI
A well-conditioned estimator for large-dimensional covariance matrices
Olivier Ledoit,Michael Wolf +1 more
TL;DR: This paper introduces an estimator that is both well-conditioned and more accurate than the sample covariance matrix asymptotically, that is distribution-free and has a simple explicit formula that is easy to compute and interpret.
Journal ArticleDOI
Regularized Discriminant Analysis
TL;DR: Alternatives to the usual maximum likelihood estimates for the covariance matrices are proposed, characterized by two parameters, the values of which are customized to individual situations by jointly minimizing a sample-based estimate of future misclassification risk.
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Optimal spatial filtering of single trial EEG during imagined hand movement
TL;DR: It is demonstrated that spatial filters for multichannel EEG effectively extract discriminatory information from two populations of single-trial EEG, recorded during left- and right-hand movement imagery.
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