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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Lei Wang Guizhi Xu Shuo Yang Miaomiao Guo Weili Yan Jiang Wang |
| Copyright Year | 2012 |
| Abstract | Brain Computer Interface (BCI) is a system that provides an artificial communication between the human brain and the external world. It may give disabled people direct control over a neuro-prosthesis by their intentions that are reflected in their brain signals. In this paper, brain electric field data (EEG) was recorded though 28 electrodes placed on the scalp. According to the fact that EEG is non-stationary and non-linear; a non-linear dynamic method called Sample Entropy (SampEn) was applied to extract the features of EEG. A Support Vector Machine (SVM) classifier was structured for pattern classification. The final results show that SampEn is an effective method to extract the feature of different brain states. |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 706238 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781467313339 |
| e-ISBN | 9781467313353 |
| DOI | 10.1109/ICEF.2012.6310370 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-06-19 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Accuracy Time series analysis Feature extraction Electroencephalography Entropy Vectors |
| Content Type | Text |
| Resource Type | Article |
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