Mining the Independent Source of ERP Components with ICA Decomposition

  • Jia-Cai Zhang
  • Xiao-Jie Zhao
  • Yi-Jun Liu
  • Li Yao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


Independent component analysis (ICA) can blindly separates the input ERP data into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain regions. In this study, we use ICA to illustrate that the P300 components in two ERPs recorded under various conditions or tasks are both mainly contributed from a few independent sources. ICA decomposition also indicates a new method to compare P300 components between two ERPs induced by two related tasks. Our comparisons are made on those independent sources contributed to the P300 components, rather than on the ERP waveforms directly. This novel approach identifies not only the similar or common independent components in both conditions that bring about a common part in ERP time courses, but also those different components induced by the different parts in ERP waveforms. Our study suggests that the ICA method is a useful tool to study the brain dynamics.


Independent Component Analysis Independent Component Independent Component Analysis Independent Source P300 Component 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jia-Cai Zhang
    • 1
  • Xiao-Jie Zhao
    • 1
  • Yi-Jun Liu
    • 2
  • Li Yao
    • 1
  1. 1.College of Information Science and TechnologyBeijing Normal UniversityBeijingChina
  2. 2.McKnight Brain InstituteUniversity of FloridaGainesvilleUSA

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