Application of quadratic optimization to multi-class common spatial pattern algorithm in brain-computer interfaces

Wei, Qingguo; Ma, Yuhui; Chen, Kui

doi:10.1109/bmei.2010.5639962

Cited by 2 publications

(1 citation statement)

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“…The idea of CSP is to map data of two classes onto the same dimension such that variance of one class is maximized while variance of the other one is minimized. Although CSP is very successful in 2-class BCI classification systems, applying it to multi-class BCI classification systems is still an open problem [11] [4].…”

Section: Introductionmentioning

confidence: 99%

Approximation-based Common Principal Component for feature extraction in multi-class Brain-Computer Interfaces

Hoang

Tran

Huang

2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Common Spatial Pattern (CSP) is a state-of-the-art method for feature extraction in Brain-Computer Interface (BCI) systems. However it is designed for 2-class BCI classification problems. Current extensions of this method to multiple classes based on subspace union and covariance matrix similarity do not provide a high performance. This paper presents a new approach to solving multi-class BCI classification problems by forming a subspace resembled from original subspaces and the proposed method for this approach is called Approximation-based Common Principal Component (ACPC). We perform experiments on Dataset 2a used in BCI Competition IV to evaluate the proposed method. This dataset was designed for motor imagery classification with 4 classes. Preliminary experiments show that the proposed ACPC feature extraction method when combining with Support Vector Machines outperforms CSP-based feature extraction methods on the experimental dataset.

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Section: Introductionmentioning

confidence: 99%