Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6950613 | Biomedical Signal Processing and Control | 2018 | 8 Pages |
Abstract
Common spatial pattern (CSP) as a feature extraction approach has been successfully applied in the field of motor imagery (MI) tasks classification. The classification performance of CSP deeply depends on the MI related channels and classifiers. However, many existing variants of CSP usually design spatial patterns by removing irrelevant or noisy distorted channels and selecting classifiers manually. In this paper, we propose a novel but simple calculation model termed information fusion scheme based CSP (IFCSP). It employs information fusion technology to take the place of conventional classifiers. Firstly, we divide all channels into several symmetrical sensor groups. Then the average Euclidean distance ratio (EDR) of each sensor group is calculated between different MI tasks following CSP. In the end, information fusion technology is employed to make the utmost of EDRs of all sensor groups to obtain the final result. In this study, the channel division scheme and parameter setting are determined by cross-validation on training data. As such, the proposed method can be customized to yield better classification accuracy. The proposed IFCSP method is validated on the well-known BCI competition IV dataset 2a. Experimental results reveal that the proposed IFCSP method outperforms other existing competitive approaches in the classification of motor imagery tasks.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Jie Wang, Zuren Feng, Na Lu, Lei Sun, Jing Luo,