Artifact suppression from EEG signals using data adaptive time domain filtering

Authors:
Md. Khademul Islam Molla;Md. Rabiul Islam;Toshihisa Tanaka;Tomasz M. Rutkowski
Affiliations:
Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh;Department of Computer Science and Engineering, Pabna Science and Technology University, Pabna, Bangladesh;Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan and Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute ...;Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Saitama, Japan and Life Science Center of Tsukuba Advanced Research Alliance, University of Tsukuba, Tsukuba, Japan
Venue:
Neurocomputing
Year:
2012

Citing 5
Cited 0

A Theory for Multiresolution Signal Decomposition: The Wavelet Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
SVD based technique for noise reduction in electroencephalographic signals

Signal Processing
Multichannel spectral pattern separation - An EEG processing application -

ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
The discrete multiple wavelet transform and thresholding methods

IEEE Transactions on Signal Processing
De-noising by soft-thresholding

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper presents a data adaptive filtering approach to separate the electrooculograph (EOG) artifact from the recorded electroencephalograph (EEG) signal. Empirical mode decomposition (EMD) technique is used to implement the time domain filter. Fractional Gaussian noise (fGn) is used here as the reference signal to detect the distinguish feature of EOG signal to be used to separate from EEG. EMD is applied to the raw EEG and fGn separately to produce a finite number band limited signals named intrinsic mode functions (IMFs). The energies of individual IMFs of fGn and that of raw EEG are compared to derive the energy based threshold for the suppression of EOG effects. The separation results using EMD based approach is also compared with wavelet thresholding technique. The experimental results show that the data adaptive filtering technique performs better than the wavelet based approach.