Removing EOG artifacts from EEG signal using noise-assisted multivariate empirical mode decomposition
Sania Zahan · 2016
Electroencephalogram (EEG) has significant applications on medical diagnosis and Brain Computer Interfacing (BCI). But the main obstacle of analyzing EEG signal is various types of noises to get actual information. Electro-oculogram (EOG) is a vital noise in EEG signal that can be produced by eye movements. De-noising EOG from EEG signal is the key issue in this research. Many research has been done on this purpose mainly Independent Component Analysis (ICA) based EOG separation with reference signal and wavelet based EOG separation. In this research, multivariate Fractional Gaussian noise channel will be used to establish a uniformly distributed reference scale and to derive the energy based threshold to detect the low frequency trends caused by EOG artifact. Avoiding these artifacts, we can get EOG free EEG hoping better results than existing methods.