Improved Transductive Support Vector Machine for a Small Labelled Set in Motor Imagery-Based Brain-Computer Interface

Joint Authors

Xu, Yilu
Hua, Jing
Zhang, Hua
Hu, Ronghua
Huang, Xin
Liu, Jizhong
Guo, Fumin

Source

Computational Intelligence and Neuroscience

Issue

Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-16, 16 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2019-11-25

Country of Publication

Egypt

No. of Pages

16

Main Subjects

Biology

Abstract EN

Long and tedious calibration time hinders the development of motor imagery- (MI-) based brain-computer interface (BCI).

To tackle this problem, we use a limited labelled set and a relatively large unlabelled set from the same subject for training based on the transductive support vector machine (TSVM) framework.

We first introduce an improved TSVM (ITSVM) method, in which a comprehensive feature of each sample consists of its common spatial patterns (CSP) feature and its geometric feature.

Moreover, we use the concave-convex procedure (CCCP) to solve the optimization problem of TSVM under a new balancing constraint that can address the unknown distribution of the unlabelled set by considering various possible distributions.

In addition, we propose an improved self-training TSVM (IST-TSVM) method that can iteratively perform CSP feature extraction and ITSVM classification using an expanded labelled set.

Extensive experimental results on dataset IV-a from BCI competition III and dataset II-a from BCI competition IV show that our algorithms outperform the other competing algorithms, where the sizes and distributions of the labelled sets are variable.

In particular, IST-TSVM provides average accuracies of 63.25% and 69.43% with the abovementioned two datasets, respectively, where only four positive labelled samples and sixteen negative labelled samples are used.

Therefore, our algorithms can provide an alternative way to reduce the calibration time.

American Psychological Association (APA)

Xu, Yilu& Hua, Jing& Zhang, Hua& Hu, Ronghua& Huang, Xin& Liu, Jizhong…[et al.]. 2019. Improved Transductive Support Vector Machine for a Small Labelled Set in Motor Imagery-Based Brain-Computer Interface. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-16.
https://search.emarefa.net/detail/BIM-1129374

Modern Language Association (MLA)

Xu, Yilu…[et al.]. Improved Transductive Support Vector Machine for a Small Labelled Set in Motor Imagery-Based Brain-Computer Interface. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-16.
https://search.emarefa.net/detail/BIM-1129374

American Medical Association (AMA)

Xu, Yilu& Hua, Jing& Zhang, Hua& Hu, Ronghua& Huang, Xin& Liu, Jizhong…[et al.]. Improved Transductive Support Vector Machine for a Small Labelled Set in Motor Imagery-Based Brain-Computer Interface. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-16.
https://search.emarefa.net/detail/BIM-1129374

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1129374