A Hierarchal Classifier for Identifying Independent Components

Chin-Teng Lin, Yu-Kai Wang, Shi-An Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Brain-computer interface (BCI) has shown explosive growth for multiple applications in the recently years. Removing artifacts and selecting useful brain sources are essential in BCI research. Independent Component Analysis (ICA) has been proven as an effective technique to remove artifacts and many brain related researches are based on ICA. However, the useful independent components with brain sources are usually selected manually according to the scalp-plots. This is great inconvenience and a barrier for real-time BCI applications of EEG. In this investigation, a two-layer automatic identification model is proposed to select useful brain sources. It is based on neural network including support vector machine with radial basis function (SVMRBF) and self-organizing map (SOM). In the first layer, SVM discriminates useful independent components from the artifact effectively. In the second layer, these selected useful components are automatically classified to different spatial brain sources according to SOM. This study suggests this model to one general application for EEG study. It can reduce the effect of subjective judgment and improve the performance of EEG analysis.
Original languageAmerican English
Title of host publication2012 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
PublisherIEEE
ISBN (Print)978-1-4673-1490-9
DOIs
StatePublished - 2012

Publication series

NameIEEE International Joint Conference on Neural Networks (IJCNN)
ISSN (Print)2161-4393

Keywords

  • component
  • Brain-computer interface
  • independent component analysis
  • Electroencephalogram
  • neural network

Fingerprint Dive into the research topics of 'A Hierarchal Classifier for Identifying Independent Components'. Together they form a unique fingerprint.

Cite this