26-06-2013, 12:32 PM
Artifact removal from EEG signals using adaptive filters in cascade
Artifact removal.pdf (Size: 616.34 KB / Downloads: 37)
Abstract
Artifacts in EEG (electroencephalogram) records are caused by various factors, like
line interference, EOG (electro-oculogram) and ECG (electrocardiogram). These noise sources
increase the difficulty in analyzing the EEG and to obtaining clinical information. For this
reason, it is necessary to design specific filters to decrease such artifacts in EEG records. In this
paper, a cascade of three adaptive filters based on a least mean squares (LMS) algorithm is
proposed. The first one eliminates line interference, the second adaptive filter removes the
ECG artifacts and the last one cancels EOG spikes. Each stage uses a finite impulse response
(FIR) filter, which adjusts its coefficients to produce an output similar to the artifacts present in
the EEG. The proposed cascade adaptive filter was tested in five real EEG records acquired in
polysomnographic studies. In all cases, line-frequency, ECG and EOG artifacts were
attenuated. It is concluded that the proposed filter reduces the common artifacts present in EEG
signals without removing significant information embedded in these records.
Introduction
EEG records carry information about abnormalities or responses to certain stimuli in the human brain.
Some of the characteristics of these signals are the frequency and the morphology of their waves.
These components are in the order of just a few up to 200 μV, and their frequency content differs
among the different neurological rhythms, as the alpha, beta, delta and theta rhythms [1].
Such rhythms are analyzed by physicians in order to detect neural disorders and cerebral
pathologies [2]. However, these rhythms are generally mixed with other biological signals, for
example alpha is commonly mixed with the EOG (electro-oculogram). In this case, opening, closing
or movements of the eyes produce artifacts in the EEG. Other artifact sources are the ECG
(electrocardiogram), EMG (electromyogram) and the power line interference (50 or 60 Hz) [3]. An
example of an EEG mixed with ECG and corrupted with line interference is illustrated in Figure 1.
Due to the presence of artifacts, it is difficult to analyze the EEG, for they introduce spikes which
can be confused with neurological rhythms. Thus, noise and undesirable signals must be eliminated or
attenuated from the EEG to ensure a correct analysis and diagnosis.
Methodology
Adaptive Filtering.
Conventional filtering cannot be applied to eliminate those types of artifacts because EEG signal and
artifacts have overlapping spectra.
Herein, we propose the use of adaptive filters, which are based on the optimization theory.
Adaptive filters have the capability of modifying their properties according to selected features of the
signals being analyzed. Figure 2 illustrates the structure of an adaptive filter. There is a primary signal
d(n) and a secondary signal x(n). The linear filter H(z) produces an output y(n), which is subtracted
from d(n) to compute an error e(n).
DISCUSSION AND CONCLUSIONS
Three adaptive filters in cascade, based on LMS algorithm, were described in order to cancel common
artifacts (line interference, ECG and EOG) present in EEG records.
The advantages of using a cascade of three filters instead of filtering the three signals with a single
adaptive filter are among others,
a) The coefficient’s adaptation in three independent filters is simpler and faster than their adaptation in
a single filter.
b) At each stage output, the error signals ei(n), EEG with one of the three attenuated artifacts are
present; such separation (by artifact) may be useful in some applications where such output might be
enough.
Advantages of adaptive filters over conventional ones include preservation of components intrinsic
to the EEG record. Besides, they can adapt their coefficients to variations in heart frequency, abrupt
changes in the line frequency (caused, say, by ignition of electric devices) or modifications due to eye
movements.