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Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting
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.
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Metadata
- Authors
- Lu, H., Eng, H.-L., Guan, C., Plataniotis, K. N., & Venetsanopoulos, A. N.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2010-12
- Volume
- 57
- Issue
- 12
- Pages
- 2936-2946
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2155617375
- Cited by
- 309
- References
- 36
- FWCI
- 4.89
- Type
- journal article
- Language
- en
- Open access
- closed
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