Application of neural network to brain-computer interface

Wei Yen Hsu, I. Jen Chiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


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 languageEnglish
Title of host publicationProceedings - 2012 IEEE International Conference on Granular Computing, GrC 2012
Number of pages6
Publication statusPublished - 2012
Event2012 IEEE International Conference on Granular Computing, GrC 2012 - HangZhou, China
Duration: Aug 11 2012Aug 13 2012


Other2012 IEEE International Conference on Granular Computing, GrC 2012


  • Active segment selection
  • Brain-computer interface (BCI)
  • Electroencephalogram (EEG)
  • Modified fractal dimension
  • Support vector machine (SVM)
  • Wavelet transform

ASJC Scopus subject areas

  • Software


Dive into the research topics of 'Application of neural network to brain-computer interface'. Together they form a unique fingerprint.

Cite this