Abstract
In this study, an neural-network-based system is proposed for the applications of brain-computer interface (BCI). Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the system consists of three procedures, including enhanced active segment selection, feature extraction, and classification. Firstly, combined with the use of continuous wavelet transform (CWT) and Student's two-sample t-statistics, the 2D anisotropic Gaussian filter is proposed to further refine the active-segment selection. Multiresolution fractal features are then extracted from wavelet data by means of modified fractal dimension. Finally, support vector machine (SVM) is used for classification. Compared with other approaches on motor imagery data, the results indicate that the proposed method is promising in BCI applications.
Original language | English |
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Title of host publication | Proceedings - 2012 IEEE International Conference on Granular Computing, GrC 2012 |
Pages | 163-168 |
Number of pages | 6 |
DOIs | |
Publication status | Published - 2012 |
Event | 2012 IEEE International Conference on Granular Computing, GrC 2012 - HangZhou, China Duration: Aug 11 2012 → Aug 13 2012 |
Other
Other | 2012 IEEE International Conference on Granular Computing, GrC 2012 |
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Country | China |
City | HangZhou |
Period | 8/11/12 → 8/13/12 |
Keywords
- Active segment selection
- Brain-computer interface (BCI)
- Electroencephalogram (EEG)
- Modified fractal dimension
- Support vector machine (SVM)
- Wavelet transform
ASJC Scopus subject areas
- Software