Image Denoising by Data Adaptive and Non-Data Adaptive Transform Domain Denoising Method Using EEG Signal - Proceedings of All India Seminar on Biomedical Engineering 2012 (AISOBE-2012) - page 8

Image Denoising by Data Adaptive
and Non-Data Adaptive Transform
Domain Denoising Method Using
EEG Signal
Vandana Roy and Shailja Shukla
Abstract This chapter proposes an automatic method for artifact removal and
noise elimination from scalp electroencephalogram recordings (EEG). The method
is based on transform domain method having combination of data adaptive and
non-data adaptive transform domain image denoising method to improve artifact
elimination (ocular, high frequency muscle, and electrocardiogram (ECG)
artifacts). The elimination of artifact from scalp EEGs is of substantial significance
for both the automated and visual examination of underlying brainwave actions.
These noise sources increase the difficulty in analyzing the EEG and obtaining
clinical information related to pathology. Hence it is crucial to design a procedure
to decrease such artifacts in EEG records. The role of a data adaptive transform
domain, i.e., ICA to separate the signal from multichannel sources, then non-data
adaptive transform, i.e., wavelet is applied to denoise the signal. The proposed
methodology successfully rejected a good percentage of artifacts and noise, while
preserving almost all the cerebral activity. The ''denoised artifact-free'' EEG
presents a very good improvement compared with recorded raw EEG.
Keywords Artifact removal Electroencephalogram (EEG) Wavelet denoising
Independent component analysis (ICA)
V. Roy ( & )
Department of Electronics and Communication, Jabalpur Engineering College, Jabalpur,
Madhya Pradesh, India
e-mail: [e-mail]
S. Shukla
Department of Computer Science Engineering, Jabalpur Engineering College, Jabalpur,
Madhya Pradesh, India