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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Qingguo Wei Meixia Xiao Zongwu Lu |
| Copyright Year | 2011 |
| Abstract | Steady-state visual evoked potentials (SSVEPs) are widely employed for target detection in brain-computer interfaces (BCIs). Canonical correlation analysis (CCA), which extends ordinary correlation to two sets of variables, is a new method for SSVEP detection. In this paper, the performance of CCA is compared with that of traditional power spectral density analysis (PSDA) in terms of power spectral amplitude, recognition accuracy, information transfer rate and operating speed. The results show that the CCA method outperforms the PSDA in all these technical indexes. |
| Starting Page | 7 |
| Ending Page | 10 |
| File Size | 292809 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781457706769 |
| DOI | 10.1109/IHMSC.2011.72 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-08-26 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Brain-computer interface Visualization Accuracy Correlation Target recognition Power spectral density analysis Harmonic analysis Electroencephalography Steady-state visual evoked potentials Canonical correlation analysis Signal to noise ratio |
| Content Type | Text |
| Resource Type | Article |
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