Joint Rayleigh coefficient maximization and graph based semi-supervised for the classification of motor imagery EEG
文献类型：会议论文
| 作者 | Guan Guan; Youpan Hu; Qing He; Bin Leng; HaiBin Wang; Hehui Zou; Wenkai Wu |
| 出版日期 | 2013 |
| 会议名称 | 2013 IEEE International Conference on Information and Automation, ICIA 2013 |
| 会议地点 | Yinchuan, China |
| 英文摘要 | Classifying electroencephalogram (EEG) signals is one of the most important issues on motor imagery-based Brain computer interfaces (BCIs). Typically, suchclassification has been performed using a small training dataset To date, most of the classification of the algorithms were proposed for large samples. In this paper, a combination of Rayleigh coefficient maximization and graph-based method was developed to classify EEG signals with small training dataset. The Rayleighcoefficient maximization was adopted to obtain the projection directions, which extract discriminating features from the preprocessed dataset. Next, both training and testing features are applied to construct an affinity matrix, and then both affinity matrix and all label information are applied to train a classifier basedon graph-based semi-supervised method. In this approach, both labeled and unlabeled samples are used for training a classifier. Hence it can be used in small training data case. Finally, a new iteration mechanism is applied to update the training data set. And the experiment results on BCI competition III dataset IVa show that the classification accuracy using our method was higher than using CSP (common spatial pattern) and support vector machine (SVM) method in all subjects with different size of training dataset We used an eightfold cross-validation on this dataset, and the results show a good stability of our algorithm. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/4557]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2013 |
| 推荐引用方式 GB/T 7714 | Guan Guan,Youpan Hu,Qing He,et al. Joint Rayleigh coefficient maximization and graph based semi-supervised for the classification of motor imagery EEG[C]. 见:2013 IEEE International Conference on Information and Automation, ICIA 2013. Yinchuan, China. |
入库方式： OAI收割
来源：深圳先进技术研究院
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。