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 |
|---|---|
| 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 |
|---|---|
| Country | China |
| City | HangZhou |
| Period | 8/11/12 → 8/13/12 |
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Keywords
- Active segment selection
- Brain-computer interface (BCI)
- Electroencephalogram (EEG)
- Modified fractal dimension
- Support vector machine (SVM)
- Wavelet transform
ASJC Scopus subject areas
- Software
Cite this
Application of neural network to brain-computer interface. / Hsu, Wei Yen; Chiang, I. Jen.
Proceedings - 2012 IEEE International Conference on Granular Computing, GrC 2012. 2012. p. 163-168 6468559.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Application of neural network to brain-computer interface
AU - Hsu, Wei Yen
AU - Chiang, I. Jen
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Active segment selection
KW - Brain-computer interface (BCI)
KW - Electroencephalogram (EEG)
KW - Modified fractal dimension
KW - Support vector machine (SVM)
KW - Wavelet transform
UR - http://www.scopus.com/inward/record.url?scp=84875039129&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84875039129&partnerID=8YFLogxK
U2 - 10.1109/GrC.2012.6468559
DO - 10.1109/GrC.2012.6468559
M3 - Conference contribution
SN - 9781467323093
SP - 163
EP - 168
BT - Proceedings - 2012 IEEE International Conference on Granular Computing, GrC 2012
ER -