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

Dat Tran; Xu Huang

doi:10.1109/EMBC.2013.6610686

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

Dat Tran, Xu Huang

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

Abstract

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

Original language	English
Title of host publication	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Editors	Jessica M. Lotito
Place of Publication	London
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	5061-5064
Number of pages	4
Volume	40
ISBN (Electronic)	9781457702167
DOIs	https://doi.org/10.1109/EMBC.2013.6610686
Publication status	Published - 2013
Event	2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Osaka, Osaka, Japan Duration: 3 Jul 2013 → 7 Jul 2013

Conference

Conference	2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Abbreviated title	EMBC 13
Country/Territory	Japan
City	Osaka
Period	3/07/13 → 7/07/13

Access to Document

10.1109/EMBC.2013.6610686

Cite this

Tran, D., & Huang, X. (2013). Approximation-based Common Principal Component for feature extraction in multi-class Brain-Computer Interfaces. In J. M. Lotito (Ed.), Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (Vol. 40, pp. 5061-5064). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/EMBC.2013.6610686

Tran, Dat ; Huang, Xu. / Approximation-based Common Principal Component for feature extraction in multi-class Brain-Computer Interfaces. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. editor / Jessica M. Lotito. Vol. 40 London : IEEE, Institute of Electrical and Electronics Engineers, 2013. pp. 5061-5064

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title = "Approximation-based Common Principal Component for feature extraction in multi-class Brain-Computer Interfaces",

abstract = "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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Tran, D & Huang, X 2013, Approximation-based Common Principal Component for feature extraction in multi-class Brain-Computer Interfaces. in JM Lotito (ed.), Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. vol. 40, IEEE, Institute of Electrical and Electronics Engineers, London, pp. 5061-5064, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, 3/07/13. https://doi.org/10.1109/EMBC.2013.6610686

Approximation-based Common Principal Component for feature extraction in multi-class Brain-Computer Interfaces. / Tran, Dat; Huang, Xu.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. ed. / Jessica M. Lotito. Vol. 40 London: IEEE, Institute of Electrical and Electronics Engineers, 2013. p. 5061-5064.

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

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N2 - 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

AB - 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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Tran D, Huang X. Approximation-based Common Principal Component for feature extraction in multi-class Brain-Computer Interfaces. In Lotito JM, editor, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Vol. 40. London: IEEE, Institute of Electrical and Electronics Engineers. 2013. p. 5061-5064 doi: 10.1109/EMBC.2013.6610686

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

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