Biomedical Engineering Reference
In-Depth Information
Introduction
The electrical activity produced by the brain is recorded by the electroencepha-
logram (EEG) using several electrodes placed on the scalp. Signal characteristics
vary from one state to another, such as wakefulness/sleep or normal/pathological.
EEG is the multivariate time series data measured using multiple sensors posi-
tioned on the scalp that imitates electrical potential produced by behaviors of brain
and is a record of the electrical potentials created by the cerebral cortex nerve
cells. There are two categories of EEG based on where the signal is obtained in the
head: scalp or intracranial. Scalp EEG being the main focus of the research, uses
small metal discs, also called electrodes, which are kept on the scalp with good
mechanical and electrical touch. Intracranial EEG is obtained by special electrodes
placed in the brain during a surgery. The electrodes should be of low impedance in
order to record the exact voltage of the brain neuron. The variations in the voltage
difference among electrodes are sensed and amplified before being transmitted to a
computer program [
1
]. Classically, five major brain waves can be distinguished by
their frequency ranges: delta (d) 0.5-4 Hz, theta (h) 4-8 Hz, alpha (a) 8-13 Hz,
beta (b) 13-30 Hz and gamma (c) 30-128 Hz. The informative cortically gener-
ated signals are contaminated by extra-cerebral artifact sources: ocular move-
ments, eye blinks, electrocardiogram (ECG), muscular artifacts. Generally, the
mixture between brain signals and artifactual signals is present in all sensors,
although not necessarily in the same proportions (depending on the spatial dis-
tribution). Moreover, the EEG recordings are also affected by other unknown
basically random signals (instrumentation noise, other physiological generators,
external electromagnetic activity, etc.) which can be modeled as additive random
noise. These phenomena make difficult the analysis and interpretation of the EEGs,
and a first important processing step would be the elimination of the artifacts and
noise. Several methods for artifact elimination were proposed. Most of them
consist of two main steps: artifact extraction from the multichannel recorded
signals, generally using some signal separation methods, followed by signal
classification. Our goal is to contribute to EEG artifact rejection by proposing an
original and more complete automatic methodology consisting of an optimized
combination of several signal processing and data analysis techniques [
2
].
This chapter is organized as follows:
Supporting Literature
briefs the supporting
literature,
Data Adaptive Transform Domain Image Denoising Method: ICA
states
the data adaptive transform domain method to separate the signals from multi-
channel sources, then
Non Data Adaptive Transform Domain Based Denoising
(Wavelet denoising)
gives details of non-data adaptive transform domain method
to denoise the signal to remove artifacts. This method assumes that EEG contains
two classes namely, artifact and non- artifact signal, and then it calculates the
optimum threshold separating these two classes.
Proposed Method
is dedicated to
our present approach to denoise the signal and
Experimental Results
presents the
main results in Sect. 6.
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