Jung, T., Makeig, S., Humphries, C., Lee, T., Mckeown, M.J., Iragui, V. and Sejnowski, T.J. (1998) Extended ICA removes artifacts from electroencephalographic recordings. Advances in Neural Information Processing Systems 10, MIT Press, Cambridge, 894-900.

has been cited by the following article:

• TITLE: Comparison of ICA and WT with S-transform based method for removal of ocular artifact from EEG signals

  AUTHORS: Kedarnath Senapati, Aurobinda Routray

  KEYWORDS: EEG, Ocular Artifact, S-transform, Wavelet Transform, Independent Component Analysis

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.4 No.5, May 17, 2011

  ABSTRACT: Ocular artifacts are most unwanted disturbance in electroencephalograph (EEG) signals. These are characterized by high amplitude but have overlap-ping frequency band with the useful signal. Hence, it is difficult to remove the ocular artifacts by traditional filtering methods. This paper proposes a new approach of artifact removal using S-transform (ST). It provides an instantaneous time-frequency repre-sentation of a time-varying signal and generates high magnitude S-coefficients at the instances of abrupt changes in the signal. A threshold function has been defined in S-domain to detect the artifact zone in the signal. The artifact has been attenuated by a suitable multiplying factor. The major advantage of ST-fil- tering is that the artifacts may be removed within a narrow time-window, while preserving the frequency information at all other time points. It also preserves the absolutely referenced phase information of the signal after the removal of artifacts. Finally, a com-parative study with wavelet transform (WT) and in-dependent component analysis (ICA) demonstrates the effectiveness of the proposed approach.