Generalised support vector machine for brain-computer interface

Trung Le; Dat Tran; Tuan Hoang; Wanli Ma; Dharmendra Sharma

doi:10.1007/978-3-642-24955-6_82

Generalised support vector machine for brain-computer interface

Trung Le, Dat Tran, Tuan Hoang, Wanli Ma, Dharmendra Sharma

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

3 Citations (Scopus)

Abstract

Support vector machine (SVM) and support vector data description (SVDD) are the well-known kernel-based methods for pattern classification. SVM constructs an optimal hyperplane whereas SVDD constructs an optimal hypersphere to separate data between two classes. SVM and SVDD have been compared in pattern classification experiments, however there is no theoretical work on comparison of these methods. This paper presents a new theoretical model to unify SVM and SVDD. The proposed model constructs two optimal points which can be transformed to hyperplane or hypersphere. Therefore SVM and SVDD are regarded as special cases of this proposed model. We applied the proposed model to analyse the dataset III for motor imagery problem in BCI Competition II and achieved promising results.

Original language	English
Title of host publication	Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings
Pages	692-700
Number of pages	9
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-24955-6_82
Publication status	Published - 2011
Externally published	Yes
Event	International Conference on Neural Information Processing 2011 - Shanghai, China Duration: 13 Nov 2011 → 17 Nov 2011 Conference number: 18th https://link.springer.com/book/10.1007/978-3-642-24958-7 (Proceedings)

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Number	PART 1
Volume	7062
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Neural Information Processing 2011
Abbreviated title	ICONIP 2011
Country/Territory	China
City	Shanghai
Period	13/11/11 → 17/11/11
Internet address	https://link.springer.com/book/10.1007/978-3-642-24958-7 (Proceedings)

Keywords

Brain-Computer Interface
Kernel Methods
Support Vector Data Description
Support Vector Machine

Access to Document

10.1007/978-3-642-24955-6_82

Cite this

@inproceedings{614543597fc34e778020a83f07fab5d1,

title = "Generalised support vector machine for brain-computer interface",

abstract = "Support vector machine (SVM) and support vector data description (SVDD) are the well-known kernel-based methods for pattern classification. SVM constructs an optimal hyperplane whereas SVDD constructs an optimal hypersphere to separate data between two classes. SVM and SVDD have been compared in pattern classification experiments, however there is no theoretical work on comparison of these methods. This paper presents a new theoretical model to unify SVM and SVDD. The proposed model constructs two optimal points which can be transformed to hyperplane or hypersphere. Therefore SVM and SVDD are regarded as special cases of this proposed model. We applied the proposed model to analyse the dataset III for motor imagery problem in BCI Competition II and achieved promising results.",

keywords = "Brain-Computer Interface, Kernel Methods, Support Vector Data Description, Support Vector Machine",

author = "Trung Le and Dat Tran and Tuan Hoang and Wanli Ma and Dharmendra Sharma",

year = "2011",

doi = "10.1007/978-3-642-24955-6\_82",

language = "English",

isbn = "9783642249549",

series = "Lecture Notes in Computer Science ",

publisher = "Springer",

number = "PART 1",

pages = "692--700",

booktitle = "Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings",

edition = "PART 1",

note = "International Conference on Neural Information Processing 2011, ICONIP 2011 ; Conference date: 13-11-2011 Through 17-11-2011",

url = "https://link.springer.com/book/10.1007/978-3-642-24958-7",

}

Le, T, Tran, D, Hoang, T, Ma, W & Sharma, D 2011, Generalised support vector machine for brain-computer interface. in Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings. PART 1 edn, Lecture Notes in Computer Science , no. PART 1, vol. 7062 , pp. 692-700, International Conference on Neural Information Processing 2011, Shanghai, China, 13/11/11. https://doi.org/10.1007/978-3-642-24955-6_82

Generalised support vector machine for brain-computer interface. / Le, Trung; Tran, Dat; Hoang, Tuan et al.
Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings. PART 1. ed. 2011. p. 692-700 (Lecture Notes in Computer Science ; Vol. 7062 , No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Generalised support vector machine for brain-computer interface

AU - Le, Trung

AU - Tran, Dat

AU - Hoang, Tuan

AU - Ma, Wanli

AU - Sharma, Dharmendra

N1 - Conference code: 18th

PY - 2011

Y1 - 2011

N2 - Support vector machine (SVM) and support vector data description (SVDD) are the well-known kernel-based methods for pattern classification. SVM constructs an optimal hyperplane whereas SVDD constructs an optimal hypersphere to separate data between two classes. SVM and SVDD have been compared in pattern classification experiments, however there is no theoretical work on comparison of these methods. This paper presents a new theoretical model to unify SVM and SVDD. The proposed model constructs two optimal points which can be transformed to hyperplane or hypersphere. Therefore SVM and SVDD are regarded as special cases of this proposed model. We applied the proposed model to analyse the dataset III for motor imagery problem in BCI Competition II and achieved promising results.

AB - Support vector machine (SVM) and support vector data description (SVDD) are the well-known kernel-based methods for pattern classification. SVM constructs an optimal hyperplane whereas SVDD constructs an optimal hypersphere to separate data between two classes. SVM and SVDD have been compared in pattern classification experiments, however there is no theoretical work on comparison of these methods. This paper presents a new theoretical model to unify SVM and SVDD. The proposed model constructs two optimal points which can be transformed to hyperplane or hypersphere. Therefore SVM and SVDD are regarded as special cases of this proposed model. We applied the proposed model to analyse the dataset III for motor imagery problem in BCI Competition II and achieved promising results.

KW - Brain-Computer Interface

KW - Kernel Methods

KW - Support Vector Data Description

KW - Support Vector Machine

UR - https://www.scopus.com/pages/publications/81855226450

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DO - 10.1007/978-3-642-24955-6_82

M3 - Conference Paper

AN - SCOPUS:81855226450

SN - 9783642249549

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BT - Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings

T2 - International Conference on Neural Information Processing 2011

Y2 - 13 November 2011 through 17 November 2011

ER -

Generalised support vector machine for brain-computer interface

Abstract

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Keywords

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