Application of competitive hopfield neural network to brain-computer interface systems

Wei Yen Hsu

Research output: Contribution to journalArticlepeer-review

50 Citations (Scopus)


We propose an unsupervised recognition system for single-trial classification of motor imagery (MI) electroencephalogram (EEG) data in this study. Competitive Hopfield neural network (CHNN) clustering is used for the discrimination of left and right MI EEG data posterior to selecting active segment and extracting fractal features in multi-scale. First, we use continuous wavelet transform (CWT) and Student's two-sample t-statistics to select the active segment in the time-frequency domain. The multiresolution fractal features are then extracted from wavelet data by means of modified fractal dimension. At last, CHNN clustering is adopted to recognize extracted features. Due to the characteristic of non-supervision, it is proper for CHNN to classify non-stationary EEG signals. The results indicate that CHNN achieves 81.9% in average classification accuracy in comparison with self-organizing map (SOM) and several popular supervised classifiers on six subjects from two data sets.

Original languageEnglish
Pages (from-to)51-62
Number of pages12
JournalInternational Journal of Neural Systems
Issue number1
Publication statusPublished - Feb 2012
Externally publishedYes


  • Brain-computer interface (BCI)
  • Competitive Hopfield neural network (CHNN)
  • Electroencephalogram (EEG)
  • Fractal dimension (FD)
  • Motor imagery (MI)
  • Wavelet transform

ASJC Scopus subject areas

  • Computer Networks and Communications


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

Cite this