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Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting

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5 Author(s)
Haiping Lu ; Inst. for Infocomm Res., Agency for Sci., Technol. & Res., Singapore, Singapore ; How-Lung Eng ; Cuntai Guan ; Plataniotis, K.N.
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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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Biomedical Engineering, IEEE Transactions on  (Volume:57 ,  Issue: 12 )