Automatic removal of artifacts from EEG data using ICA and exponential analysis

  • Authors:

  • Ning-Yan Bian;Bin Wang;Yang Cao;Liming Zhang

  • Affiliations:

  • Department of Electronics Engineering, Fudan University, Shanghai, China;Department of Electronics Engineering, Fudan University, Shanghai, China;Department of Physiology and Biophysics, School of Life Science, Fudan University, Shanghai, China;Department of Electronics Engineering, Fudan University, Shanghai, China

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
  • Year:
  • 2006

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Abstract

Eye movements, cardiac signals, muscle noise and line noise, etc. present serious problems for the accuracy of Electroencephalographic (EEG) analysis. Some research results have shown that independent component analysis (ICA) can separate artifacts from multichannel EEG data. Further, considering the nonlinear dynamic properties of EEG signals, exponential analysis can be used to identify various artifacts and basic rhythms, such as α rhythm, etc., from each independent component (IC). In this paper, we propose an automatic artifacts removal scheme for EEG data by combining ICA and exponential analysis. In addition, the proposed scheme can also be used to detect basic rhythms from EEG data. The experimental results on both the simulated data and the real EEG data demonstrate that the proposed scheme for artifacts removal has excellent performance.