Fuzzy Hopfield neural network clustering for single-trial motor imagery EEG classification

Wei Yen Hsu

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)


An electroencephalogram (EEG) analysis system for single-trial classification of motor imagery (MI) data is proposed in this study. Unsupervised fuzzy Hopfield neural network (FHNN) clustering, together with active segment selection and multiresolution fractal features, is used in the classification of left and right MI data. Active segment selection is used to obtain the active segment in the time-scale domain with the continuous wavelet transform (CWT) and Student's two-sample t-statistics. The multiresolution fractal features are then extracted from the discrete wavelet transform (DWT) data by using the modified fractal dimension. Finally, FHNN clustering is used as the discriminant of multiresolution fractal features. FHNN clustering is capable of making flexible partitions of a finite data set, and it is an unsupervised and robust approach suitable for the classification of non-stationary biomedical signals. Compared with several popular supervised classifiers, FHNN clustering achieves promising results in classification accuracy.

Original languageEnglish
Pages (from-to)1055-1061
Number of pages7
JournalExpert Systems with Applications
Issue number1
Publication statusPublished - Jan 2012
Externally publishedYes


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

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • General Engineering


Dive into the research topics of 'Fuzzy Hopfield neural network clustering for single-trial motor imagery EEG classification'. Together they form a unique fingerprint.

Cite this