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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Bin Xia Wenlu Yang Xiao Dianyun Wang Cong |
| Copyright Year | 2010 |
| Description | Author affiliation: Department of Electronic Engineering, Shanghai Maritime University, China (Wenlu Yang; Xiao Dianyun; Wang Cong) || College of Electronics and Information Engineering, Tongji University, Shanghai, China (Bin Xia) |
| Abstract | In this paper, we discuss the different train strategies for brain-computer interface. In general, it will take a long time to train subjects for motor imagery based BCI. There are many biofeedback methods to train subject. To compare the efficiency of different training strategies, we train the subjects by using two type training models: virtual reality and progress bar. The progress bar based training strategy show good result in our experiments. |
| Starting Page | 2190 |
| Ending Page | 2193 |
| File Size | 279602 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424459582 |
| e-ISBN | 9781424459612 |
| DOI | 10.1109/ICNC.2010.5583993 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-08-10 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Support vector machines Accuracy Virtual reality virtual-reality Signal processing Feature extraction motor imagery Electroencephalography progress bar |
| Content Type | Text |
| Resource Type | Article |
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