An Efficient Multi-Class MI Based BCI Scheme Using Statistical Fusion Techniques of Classifiers

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

Standard

An Efficient Multi-Class MI Based BCI Scheme Using Statistical Fusion Techniques of Classifiers. / Lopez, Paula Sanchez; Iversen, Helle K.; Puthusserypady, Sadasivan.

Proceedings of the TENCON 2019: Technology, Knowledge, and Society. IEEE, 2019. p. 378-382 8929345 (IEEE Region 10 Annual International Conference, Proceedings/TENCON, Vol. 2019-October).

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

Harvard

Lopez, PS, Iversen, HK & Puthusserypady, S 2019, An Efficient Multi-Class MI Based BCI Scheme Using Statistical Fusion Techniques of Classifiers. in Proceedings of the TENCON 2019: Technology, Knowledge, and Society., 8929345, IEEE, IEEE Region 10 Annual International Conference, Proceedings/TENCON, vol. 2019-October, pp. 378-382, 2019 IEEE Region 10 Conference: Technology, Knowledge, and Society, TENCON 2019, Kerala, India, 17/10/2019. https://doi.org/10.1109/TENCON.2019.8929345

APA

Lopez, P. S., Iversen, H. K., & Puthusserypady, S. (2019). An Efficient Multi-Class MI Based BCI Scheme Using Statistical Fusion Techniques of Classifiers. In Proceedings of the TENCON 2019: Technology, Knowledge, and Society (pp. 378-382). [8929345] IEEE. IEEE Region 10 Annual International Conference, Proceedings/TENCON Vol. 2019-October https://doi.org/10.1109/TENCON.2019.8929345

Vancouver

Lopez PS, Iversen HK, Puthusserypady S. An Efficient Multi-Class MI Based BCI Scheme Using Statistical Fusion Techniques of Classifiers. In Proceedings of the TENCON 2019: Technology, Knowledge, and Society. IEEE. 2019. p. 378-382. 8929345. (IEEE Region 10 Annual International Conference, Proceedings/TENCON, Vol. 2019-October). https://doi.org/10.1109/TENCON.2019.8929345

Author

Lopez, Paula Sanchez ; Iversen, Helle K. ; Puthusserypady, Sadasivan. / An Efficient Multi-Class MI Based BCI Scheme Using Statistical Fusion Techniques of Classifiers. Proceedings of the TENCON 2019: Technology, Knowledge, and Society. IEEE, 2019. pp. 378-382 (IEEE Region 10 Annual International Conference, Proceedings/TENCON, Vol. 2019-October).

Bibtex

@inproceedings{52f0bb373ab74b109f68c7fbb2e43395,
title = "An Efficient Multi-Class MI Based BCI Scheme Using Statistical Fusion Techniques of Classifiers",
abstract = "An efficient implementation of a multi-class motor imagery (MI) brain computer interface (BCI) classification scheme is presented in this work. The proposed method uses the common spatial pattern (CSP) and filter bank CSP (FBCSP) algorithms, with both one versus all (OVA) and one versus one (OVO) approach for multi-class extension. Mutual information (MInf) based feature selection algorithm has been used to obtain the features to train different linear discriminant analysis (LDA) classifiers. To improve the performance, the outputs of these classifiers are combined using two statistical methods: the mode of the OVA and OVO classifiers, and the more sophisticated Dempster-Shafer (DS) theory. The method has been evaluated on the 4-class MI dataset (BCI competition IV 2a), and the results showed that it has outperformed the winner of the competition (maximum kappa value of 0.593 vs 0.569). The proposed method proved the benefits of combining classifiers with appropriate techniques.",
keywords = "Brain Computer Interface, Common Spatial Pattern, Dempster-Shafer theory, Multi-class Motor Imagery, Mutual Information",
author = "Lopez, {Paula Sanchez} and Iversen, {Helle K.} and Sadasivan Puthusserypady",
year = "2019",
doi = "10.1109/TENCON.2019.8929345",
language = "English",
series = "IEEE Region 10 Annual International Conference, Proceedings/TENCON",
publisher = "IEEE",
pages = "378--382",
booktitle = "Proceedings of the TENCON 2019",
note = "2019 IEEE Region 10 Conference: Technology, Knowledge, and Society, TENCON 2019 ; Conference date: 17-10-2019 Through 20-10-2019",

}

RIS

TY - GEN

T1 - An Efficient Multi-Class MI Based BCI Scheme Using Statistical Fusion Techniques of Classifiers

AU - Lopez, Paula Sanchez

AU - Iversen, Helle K.

AU - Puthusserypady, Sadasivan

PY - 2019

Y1 - 2019

N2 - An efficient implementation of a multi-class motor imagery (MI) brain computer interface (BCI) classification scheme is presented in this work. The proposed method uses the common spatial pattern (CSP) and filter bank CSP (FBCSP) algorithms, with both one versus all (OVA) and one versus one (OVO) approach for multi-class extension. Mutual information (MInf) based feature selection algorithm has been used to obtain the features to train different linear discriminant analysis (LDA) classifiers. To improve the performance, the outputs of these classifiers are combined using two statistical methods: the mode of the OVA and OVO classifiers, and the more sophisticated Dempster-Shafer (DS) theory. The method has been evaluated on the 4-class MI dataset (BCI competition IV 2a), and the results showed that it has outperformed the winner of the competition (maximum kappa value of 0.593 vs 0.569). The proposed method proved the benefits of combining classifiers with appropriate techniques.

AB - An efficient implementation of a multi-class motor imagery (MI) brain computer interface (BCI) classification scheme is presented in this work. The proposed method uses the common spatial pattern (CSP) and filter bank CSP (FBCSP) algorithms, with both one versus all (OVA) and one versus one (OVO) approach for multi-class extension. Mutual information (MInf) based feature selection algorithm has been used to obtain the features to train different linear discriminant analysis (LDA) classifiers. To improve the performance, the outputs of these classifiers are combined using two statistical methods: the mode of the OVA and OVO classifiers, and the more sophisticated Dempster-Shafer (DS) theory. The method has been evaluated on the 4-class MI dataset (BCI competition IV 2a), and the results showed that it has outperformed the winner of the competition (maximum kappa value of 0.593 vs 0.569). The proposed method proved the benefits of combining classifiers with appropriate techniques.

KW - Brain Computer Interface

KW - Common Spatial Pattern

KW - Dempster-Shafer theory

KW - Multi-class Motor Imagery

KW - Mutual Information

U2 - 10.1109/TENCON.2019.8929345

DO - 10.1109/TENCON.2019.8929345

M3 - Article in proceedings

AN - SCOPUS:85077674580

T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON

SP - 378

EP - 382

BT - Proceedings of the TENCON 2019

PB - IEEE

T2 - 2019 IEEE Region 10 Conference: Technology, Knowledge, and Society, TENCON 2019

Y2 - 17 October 2019 through 20 October 2019

ER -

ID: 241597271