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Application of LMS adaptive predictive filtering for muscle artifact (noise) cancellation from EEG signals
Pergamon
0045-7906(95)ooo30-5
Computers Elect. Engng Vol. 22, No. 1, pp. 13-30, 1996 Copyright 0 1996 Elsevier Science Ltd Printed in Great Britain. All rights reserved 00457906/96 %/5.00 + 0.00
APPLICATION OF LMS ADAPTIVE PREDICTIVE FILTERING FOR MUSCLE ARTIFACT (NOISE) CANCELLATION FROM EEG SIGNALS S. V. NARASIMHANt Department
of Electrical
and D. NARAYANA
Communication Engineering, Bangalore 560012, India
Indian
DUTTf
Institute
of Science.
(Received for publication 4 October 1995) Abstract-The presence of muscle artifact (noise) affects the electroencephalograph (EEG) analysis. This paper deals with the filtering of the muscle artifact (noise) from a muscle artifact contaminated EEG, by a hybrid approach. In this, the muscle artifact component outside the EEG band is removed by lowpass filtering and the component within the EEG band by the least mean square gradient adaptive predictive filtering. Further, the effect of the muscle artifact on the parametric representation of EEG and the improvement achieved by the proposed filtering, are considered for simulated and real EEG data. The results indicate that the proposed filtering facilitates a reasonably valid parametric representation of EEG even when it is contaminated with the muscle artifact. The adaptive predictors realized by tapped delay line and lattice structures have been considered. Key words: Least mean square adaptive cancellation.
algorithm,
predictive
filtering, EEG analysis,
muscle artifact,
noise
1. INTRODUCTION Electroencephalograph (EEG), the manifestation of the brain’s function, comprises of the widesense stationary stochastic component known as the background activity [viz. 6 (1-3 Hz), 0 (47 Hz), c( (8-13 Hz) and fl (14-30 Hz) activities] and the nonstationary paraxysmals (spikes and spike-andwaves) which are considered to be superimposed on the stationary component. Further, often an EEG registration is contaminated with extracerebral signals called artifacts and these may be due to muscle activity, ECG pickup, eyemovement, improper electrode contact, etc. These extracerebral signals play the role of noise for EEG background activity. Though in visual analysis, these artifacts are detected and the EEG is interpreted with some difficulty, they pose a major problem for the computer analysis of EEG. Of the many artifacts mentioned above, the muscle artifact is very common and may be many times larger in magnitude than the EEG signal. This artifact is due to contraction of neck and scalp muscles. Further, in a tense subject, the muscle activity is often wide spread though maximal in temporal regions. Artifacts in general and muscle artifact in particular, affect both time domain analysis, such as correlation or slope descriptors which are very sensitive to noise [l], and the frequency domain analysis (spectral analysis), by changing the spectral parameters like bandwidth, percentage of power distribution [2]. The muscle artifact is found to affect the EEG spectrum above 14 Hz considerably [3], i.e. the B-activity. In visual analysis, an attempt is often made to reduce the muscle artifact by decreasing the higher cutoff frequency of the recording amplifiers [4]. Hence, in general and in particular for automation of clinical EEG, removal or minimization of muscle artifact from EEG is mandatory. For this purpose, techniques such as analog filtering [5], nonlinear filtering 161,Kalman filtering [7] and time domain filtering [8] have been reported. However, the most common and simple least mean square (LMS) gradient adaptive algorithm which has been extensively used for noise cancelling applications in fields like speech, ECG, etc. [9], has not been used for muscle artifact cancellation from EEG. In this paper, the LMS adaptive predictive filtering has been proposed for muscle noise cancellation from EEG. The muscle noise cancellation is achieved by a hybrid approach which uses TPresent address: Aerospace Electronics Division, National $To whom all correspondence should be addressed.
Aerospace
I3
Laboratories,
Bangalore
560017, India.
14
S. V. Narasimhan
and D. Narayana
Dutt
lowpass filtering and adaptive predictive filtering. Further, the effect of muscle artifact on the parametric representation of EEG and the improvement achieved by the proposed filtering to this end are brought out. The results with simulated and real EEG indicate that the proposed filtering is very effective in reducing the muscle artifact and as a result the improvement achieved in the parametric representation of the EEG is of significance. The gradient adaptive predictive filtering has been realized both by tapped delay line (TDL) and lattice structures. In the following sections, Section 2 deals with the muscle artifact, Section 3 with the proposed method of filtering of muscle artifact from EEG background activity and Section 4 with the application of the proposed filtering and the results obtained for simulated and real EEG. _. 7 THE
MUSCLE
ARTIFACT
The muscle artifact is the result of superposition of large number of action potentials and each action potential forms a muscle spike. The simulation of muscle artifact is based on the model used by Johnson et al. [6] and the muscle spike is assumed to be an impulse response of a second order linear system given by
where h = ( 1/2&)exp(
- 27raT )sin(2&
T)
LI, = 2 exp( - 27coT )cos(2r& T ) ii: = exp( -- 47-0~7‘) T is the sampling period, o is the half bandwidth andf, is the frequency of the spectral peak. For a typical muscle spike, the second order system has a centre frequency at 70 Hz and a bandwidth 70 Hz. In the muscle artifact, the amplitude and duration of the spikes vary much more than the shape of the spike. The variability in the duration of the spikes can be achieved by time scaled version of the typical muscle spike and the scale factors can be 1.5 and 2.0. The different types of spikes occur with equal probability and the average rate of occurrence of the muscle spike has been found to be approx. 50 spikes per second. This is realized by driving the linear systems by independent Poisson noise generators having approximately one third the average rate (i.e. 15 spikes/s). 3.
FILTERING
OF
THE
MUSCLE
ARTIFACT
FROM
EEG
The spectra of the muscle artifact and the EEG background activity have maximum overlap around 30 Hz [6] and the muscle spectrum extends upto folding frequency. Hence, the muscle artifact can be considered to be made up of two components: (1) the out of band component (outside the EEG frequency band); and (2) the inband component (within the EEG frequency band). In the proposed method of filtering of muscle artifact, the out of band component is removed by a lowpass filter and subsequently the inband component by an adaptive filter. The out of band component is removed by a fourth order Butterworth lowpass filter having cutoff frequency at 35 Hz. This is based on the fact that EEG background activity spectrum is confined to 30 Hz [l]. This lowpass filter has a fairly linear phase characteristic at least within the EEG band of interest. In this study, an equivalent digital infinite impulse response filter [lo] is considered. In practice, a fourth order Butterworth active lowpass filter can be used either in addition to the antialiasing filter or as an antialiasing filter itself. Further, any phase distortion introduced by it due to small amount of non-linearity in its phase characteristic can be compensated by an equalizer [I 11. After lowpass filtering, the inband muscle artifact component is removed by least mean square adaptive predictive filtering. Such a filtering is possible, provided the signal bandwidth is significantly less than the bandwidth of the additive noise [12]. The inband component of the muscle artifact plays the role of the wide band noise and the EEG plays the role of a narrow band signal. The linear prediction filter schematic is shown in Fig. 1. The linear prediction filter coefficients are
LMS adaptive predictive filtering
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S. V. Narasimhan
16
(c)
and D. Narayana
Dutt
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Fig. I (a) Impulse response of tilters generating different types of muscle spikes with bandwidths: 70 Hz+O); 46.67 Hz+A) and 35 Hz-( x ). (b) Logmagnitude spectra of the impulse response of filters generating the muscle spikes with bandwidths: 70 Hz--(l); 46.67 HZ-(~) and 35 HZ-(~). (c) Simulated muscle artifact (sampled at 200 Hz). (d) Spectral density of the simulated muscle artifact (sampled at I kHz).
chosen such that the power of the error signal ~(a) between the predictor output (estimate) a(n) and the actual signal x(n), is minimized. The delay D represents the prediction distance of the filter. The noise suppression is due to the fact that the decorrelation time for broad band noise is much smaller than that of a narrow band signal. This enables the choice of the value of D which will effectively decorrelates the broad band noise and also prevents the noise appearing in the predictor output. To explain in physical terms, due to high correlation, prediction of an EEG signal sample from its previous samples is valid. Whereas, such a prediction is not valid for muscle artifact, as it is a very rapidly changing signal (poor correlation). An adaptive version of the linear prediction filter has been successfully used for processing noisy speech [13]. In the present study, the LMSTDL and LMS-lattice gradient adaptive predictors are applied for muscle artifact removal. A brief description of the two predictors from the point of view of noise cancellation is given below.
LMS adaptive predictive filtering
3.1. The LMS-TDL
17
predictor
Figure l(a) itself forms the schematic of the LMSTDL predictor. The estimate, Z(n) of the present input sample x(n) is based on the P previous input samples which are D samples away from the present instant [13,14]. That is c?(n) = i
-k
a,x(n
- D + 1).
(2)
k=l
By LMS gradient adaptation
rule,
&(n) = &(fi - 1) + 2p&(n -
1)X@
-D),
-k
k=l,2,...,P
(3)
where E(n) =x(n)
-i(n),
i ---O<<
< y < 1.
y is the forgetting factor. This is the commonly used LMS-TDL algorithm. A variation in the definition of p is given by, p = q/E,(n), 0 < q < 1, where q = i/P (i.e. in the sense 4 is sufficiently small) and the division of q by E, takes care of the nonstationary nature of the input. This definition of p is used in the present study. 3.2. The LMS-lattice
predictor
Figure l(b) shows the basic lattice filter. f,(n) and b,(n) are the forward and backward prediction errors at stage m and at time instant n. K,(n) is the reflection coefficient at stage m and instant n. The decorrelation delay D is introduced in the backward error path [14]. The lattice relations are given by J,(n) =x(n),
b,(n - 1) = x(n -D);
fm(n)=fm-,(n)+K,(n)b,-,(n Un)=~,-,(n
- 1)9
- l)+Kz(nK-1(n)
and jK,(n)] < 1, m = 1,2,3, . . . , P, for all n. The adaptation
rule for the reflection coefficient K,,,(n) is given by
K& + 1)= &l(n) - %l(n + 1)
a-L
aKm(n)
(4)
where J%,= [f;(n)
&n(n)=
+ b%)1/2.0,
v
,o
J%,-,(n - 1)
and E,(n)
= yE,(n - 1) + [f:(n)
+ b;(n
- l)],O < y < 1.
The coefficients in equation (2) are computed from the reflection coefficients K,,,(n), using Levinson’s recursion relation [15] and equation (2) gives the predictor output.
S. V Narasimhan
and D. Narayana
Dutt
---_____-1.
Adaptive
alOor
ithm
(a)
f, Cnl
f, (nl
L- ~~.._~._.._..__
__
Fig. 2. (a) The LMS -TDL
4.
._
predictor
f,*
*
( _I
predictor
with
_l?! _-.. ._ with decorrelation delay. decorrelation delay.
APPLlCATION
AND
The performance of the LMS-TDL and LMSlattice muscle artifact from muscle artifact contaminated EEG, presented here. 4.1. Studies with simulated
f (Ill
(n)
(b) The LMS
lattice
RESULTS adaptive predictors both for simulated
for the removal of and real signals are
data
The required muscle artifact is generated by filtering independent Poisson random number sequences by transfer functions corresponding to different types of spikes and adding the filter outputs with appropriate gain factors. As already mentioned, the three types of spikes, each characterized by the transfer function (1) and realized by bandwidths 70, 46.67 and 35 Hz and center frequency 70 Hz (in each case) are considered. Their corresponding impulse responses and the frequency responses are shown in Figs 2(a) and (b). The sampling rate used is I KHz. The muscle artifact thus generated and its spectral density function are shown in Figs 2(c) and (d), respectively. This muscle data [Fig. 2(c)] will be added to EEG data to get muscle artifact contaminated EEG. For the purpose of illustrating the removal of muscle noise, a typical EEG signal having specifications given in Table 1 [16] and sampled at frequency 200 Hz is considered. Further, the muscle noise generated is down sampled to 200 Hz and added to the EEG data. The choice of this sampling frequency is based on the studies carried out by Bartoli and Cerutti [7] and Gevins et al. [17]. The EEG to muscle artifact power ratio is - 2.48 dB. The EEG signal and the muscle artifact contaminated EEG are shown in Figs 3(a) and (c), respectively. Their corresponding spectra are shown in Figs 4(a) and (b). The original EEG signal itself is filtered by the Butterworth lowpass Table Activity il R 6
I.
Stxcitication
of the simulated
EEG s~anal
0 (Hz)
/1 (Hz)
% of power
0.58 I.36
10.25 1891) 0.00
63 4 33
I .27
(D is the half bandwidth for a and fl-activities and full bandwidth for ~5-activity. .& is the centre frequency)
LMS adaptive
predictive
filtering
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Fig. 3. (a) The simulated EEG signal. (b) the lowpass filtered simulated EEG signal, (c) the muscle artifact contaminated EEG, (d) the lowpass filtered muscle artifact contaminated EEG, (e) the LMSTDL predictor output, (f) the LMS-lattice predictor output.
filter in order to assess its effect on the signal. It is evident that the filtered and original signals are almost identical except for the small time shift introduced by the lowpass filter [Figs 3(a) and (b)]. Their spectra are also identical within the EEG band [Fig. 4(a)]. The muscle artifact contaminated EEG is filtered by the lowpass filter. The lowpass filtered signal is only free from the out of band EEG muscle noise component but not completely free from the muscle artifact, and the remaining noise is due to inband component [Figs 3(d) and 4(b)]. By subjecting the lowpass filtered output to adaptive filtering, the remaining noise is removed. The order of the adaptive filter P used is 10 and the decorrelation delay D is 20 samples. The values of c and y used are 0.2 and 0.92, respectively. Further, q = v = C/P. Figures 3(e) and 3(f)
S. V. Narasimhan
20
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Fig. 4. (a) Logmagnitude spectra for: EEG signal-(O); spectra for: muscle artifact contaminated EEG--(O), EEG-(A).
Dutt
lowpass lowpass
filtered filtered
48
.co
120.00
EEG-_(A). (b) Logmagnitude muscle
artifact
contaminated
indicate that the adaptive filters will converge only after 0.25 s (approximately). After convergence, the adaptive filtered signals have close resemblance in their nature to the original EEG [Fig. 3(b)]. The LMS-lattice output appears to have somewhat more noise than the output of LMS-TDL. In practice, often such a convergence time will be small as the predictive filter would have originally adapted to the existing EEG data and only the muscle noise superimposes suddenly on it. In this study, in order to find the effect of the muscle artifact on the parametric representation of EEG and the improvement achieved by the proposed filtering; original EEG, the muscle artifact
LMS adaptive predictive filtering
21
contaminated EEG, the lowpass filtered signal and the adaptive filtered outputs are subjected to Burg’s spectral estimation [15]. For this purpose, a 15th order prediction filter has been used and to ensure convergence of adaptive filter outputs, in the signal sequence, later 512 samples are considered leaving the initial 240 samples. For the signals at other stages of filtering, the corresponding data block of 512 points are used. Figure 5 shows the respective Burg’s spectra. It is seen that for the muscle artifact contaminated EEG signal, the Burg’s spectrum does not indicate
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Fig. 5. (a) Burg spectra for: lowpass filtered simulated EEG-Q), muscle artifact contaminated EEG--(2), lowpass filtered muscle artifact contaminated EEG-(3), LMS-TDL predictor output-(4). (b) Burg spectra for: lowpass filtered simulated EEG-(I), muscle artifact contaminated EEG-(2), lowpass filtered muscle artifact contaminated EEG-(3), LMS-lattice predictor output-+4).
22
S Table 2. Estimated
V.
Narasimhan and D. Narayana Dutt
frequency by Burg’s method of spectral estimatmn of filtering (for simulated EEG)
S1gKIls
(I) (2) (3) (4) (5)
for signals at different stages
a-activity peak frequency (Hz)
/J-activity peak frequency (Hz)
10.742 IO.547 IO.156 IO.352 10.742
20.1 I7 ml 20.308 19 726 IY 922
EEG (Original) EEG + Muscle artifact Lowpass filtered (EEG + Muscle amfact) LMS-TDL predictor output LMS-lattice predictor output
the peak corresponding to j-activity at all. For the lowpass filtered signal, though there is a peak corresponding to P-activity. this peak is almost at the same level as the noise peak. But after subsequent adaptive filtering, all the peaks are clearly brought out by the Burg’s method and the p-activity peak is much above the noise level. This indicates that the Burg’s parametric representation severely gets affected by the muscle noise and the proposed filtering method alleviates this problem. The performance of the two adaptive predictors are almost same. However, for the LMS-lattice predictor output, the noise level is somewhat higher than that of the LMS-TDL. This is in agreement with the time domain observation [Figs 3(e) and 3(f)]. The frequencies estimated by Burg’s method at different stages of filtering are listed in Table 2. The accuracy of peak frequency estimation of a and p-activity peaks is approximately same for lowpass filtered output and for both the predictor outputs. However, prior to any filtering, the Burg’s spectral estimation does not indicate a-acitivty peak and hence its frequency. In the above illustration, the Burg’s spectrum of the lowpass filtered EEG signal [Fig. 3(b)] is taken as reference for comparison instead of the Burg’s spectrum of the original EEG. This is due to the fact that the Burg’s spectrum for filtered EEG signal and for the original EEG will be different as the method is very sensitive to noise. The original EEG signal, which is simulated by filtering the white Gaussian noise by filters occupying different frequency bands of the EEG spectrum will have some noise up to the folding frequency. But in the lowpass filtered signal this noise is removed. This reference for comparison is valid as the lowpass filtered EEG and the original EEG are identical within the EEG band [Fig. 4(a)]. Further, since for the lowpass filtered simulated in the spectrum
!a)
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Fig. 6. (a) Logmagnitude spectrum for 1Sth order LMS-TDL predictor output (simulated EEG). Spectra for: (O)-lowpass filtered EEG; (A)-10th order predictor output; (x )-15th order predictor output. (b) Logmagnitude spectrum for 15th order LMS-lattice predictor output (simulated EEG). Spectra for: (O)-lowpass filtered EEG, (A)-10th order predictor output; ( x )--15th order predictor output.
40 .oo
LMS adaptive predictive filtering
23
EEG, the Burg’s method has been found to provide a better spectral estimate (in particular, the B-activity spectral peak) with a 15th order than with a 10th order, the 15th order has been used for performance evaluation though the adaptive predictor used is of 10th order. The effect of higher decorrelation delay D on the performance of the adaptive predictors has also been assessed. A higher delay will result in a decrease on the magnitude of the /?-acitivty peak in the spectrum of the adaptive filtered signal. This has been verified for a delay of 30 sample points with a 15th order prediction filter. For LMSTDL predictor, the decrease in p-activity spectral peak magnitude is significant compared to that of LMS-lattice [Figs 6(a) and (b)].
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24 IN
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Fig 7. (a) Muscle artifact contaminated real EEG segment; (b) the lowpass filtered muscle artifact contaminated EEG; (c) the LMS-TDL predictor output; (d) the LMSlattice predictor output; (e) Burg’s spectra for: Muscle artifact contaminated real EEG-_(I), lowpass filtered muscle artifact contaminated EEG-(2). LMS-TDL predictor output-(3). LMSlattice predictor output-(4).
The adaptive predictors though bring out the p-activity peak well above the noise level, they seem to slightly decrease the S-activity peak magnitude (Figs 5 and 6). 4.2. Studies with real EEG data The real EEG data has been recorded using a Schlumberger 5521/2) with a tape speed of (I S/S) in/s. Further, the analog
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LMS adaptive predictive filtering
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Fig. 8. (a) Muscle artifact contaminated real EEG segment; (b) the lowpass filtered muscle artifact contaminated EEG; (c) the LMS-TDL predictor output; (d) the LMS-lattice predictor output; (e) Burg’s spectra for signals shown in Fig. 8(a)--(l), Fig. S(b)-+), Fig. S(c)--(3), Fig. ll(db(4).
digital conversion) by a RIP-1 1 based hybrid computer and the digital data is stored in a magnetic tape. The digitization is done at a sampling rate of 200 Hz using a 13 bit (12 bits + 1 sign bit) analog to digital converter. For the purpose of illustration, a data segment of an alcoholic patient of age 53 yr recorded from Fpl-Fp2 electrode pair with the high frequency cut off at 120 Hz on the EEG machine, is considered. This case is particularly chosen with an intention that the EEG activity will contain predominantly the B-activity and it is this activity that gets affected by the muscle noise to a greater extent than a and &activities.
S. V. Narasimhan and D. Narayana Dutt
26
Figure 7(a) shows the real EEG signal for which the mean is removed. This is filtered by the 4th order Butterworth lowpass filter and the filtered signal is shown in Fig. 7(b). The outputs of the adaptive filters are shown in Figs 7(c) and 7(d) when the lowpass filter output is fed as input to them. The adaptive filters have the same parameters as in simulation study. Figure 7(e) shows the Burg’s spectra for the adaptive filter outputs, It also incorporates the spectra for the original EEG (mean removed) and that for the lowpass filtered signal, for comparison. In computing the Burg’s spectra as in simulation study, the later 512 data samples are considered leaving the initial 240 samples in each case.
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Fig. 9. (a) Muscle artifact contaminated real EEG segment; (b) the lowpass filtered muscle artifact contaminated EEG; (c) the LMS-TDL predictor output; (d) the LMS-lattice predictor output; (e) Burg’s spectra for signals shown in Fig. 9(a)--(I), Fig. 9(b)-(2), Fig. 9(c)--(3); (f) Burg’s spectra for signals shown in Fig. 9(a)--(l), Fig. 9(b)--(2), Fig. 9(d)--(3).
It is seen that the adaptive filters are very effective in removing the muscle noise and the lowpass filter alone is not sufficient [Figs 7(a)-(d)]. Also, in the Burg spectrum for the muscle artifact contaminated EEG [Fig. 7(e)], the /?-activity peak is not brought out clearly and it is almost at noise level and the peak occurs at 22.65 Hz. With lowpass filtering, though the /?-activity peak is brought out, it is associated with additional peaks of approximately the same magnitude and are due to inband muscle noise. These peaks occur at 19.53, 26.76 and 36.32 Hz. But for the adaptive
28
S. V. Narasimhan
and D. Narayana
Dutt
signals, the p-activity peak is brought out clearly and the additional peaks due to inband muscle noise are reduced significantly and only one peak is detected by the peak and LMS-lattice. picking algorithm. This peak occurs at 20.31 and 19.14 Hz for LMS-TDL respectively. The general applicability of the proposed method has been ascertained by considering other EEG data segments (of different subjects) and the results obtained (Figs 8 and 9) are found to be in agreement with the above illustration of real EEG data. It is important to note that with real EEG signals, there is no appreciable decrease in d-activity due to adaptive filtering, unlike that with the simulated EEG. In evaluating the performance of the proposed filtering, the Burg’s spectral estimator is used as reference and this is relevant due to the fact that the sequential adaptive predictors and the Burg’s spectral estimator are based on the same model. viz. the all-pole model and further, the latter provides an optimum estimate for the model assumed. This study clearly indicates that even in the case of real data, the parametric estimation gets affected by the muscle noise (both outside and inside EEG frequency band) and the proposed method minimizes the muscle noise significantly and enables the Burg’s parametric estimation to provide valid results. Generally. the results obtained with real data are consistent with those of simulated data at different stages of filtering. The present study has only aimed at exploring the possibility of applying the LMS predictive filtering for muscle noise cancellation. However. no efforts have been made to evaluate the comparative performance of this method in removing the muscle noise with the existing ones such as Kalman filtering [7] and non-linear filtering [6]. The study indicates that the proposed filtering provides satisfactory results from the point of view of parametric spectral estimation and is computationally efficient compared to other methods mentioned. In particular. it is quite satisfactory in bringing out the {j-activity spectral peak above the noise level in the Burg’s spectrum. This is of significance, since. it is the /f-activity that gets affected by the muscle artifact to the maximum extent. The LMSTDL and the LMS-lattice provide almost same performance. Of the two. LMS -TDL is computationally simpler and hence can be preferred to lattice. In the literature. the performance of the existing methods of muscle noise cancellation from EEG (mentioned), have not been assessed from the point of view of parametric spectral modeling. The present study establishes that the proposed filtering provides reasonably valid results even from this point of view and this is an additional information about the utility of sequential adaptive algorithms for muscle noise cancellation. Further, from the spectral estimation point of view, the study emphasises that mere lowpass filtering [2] is not sufficient and some type of adaptive filtering is essential to remove the muscle noise component within the EEG frequency band. In the present study. though the proposed filtering has been used for muscle noise cancellation from EEG background activity. a question may arise is that whether this filtering can distinguish between a muscle spike and an epileptic spike. Certainly, this distinction is not possible, since, any linear filtering is blind to different types of spikes and so is the proposed filtering. The muscle noise cancellation methods mentioned [5-81 also have this problem, However, with the nonlinear filtering [8]. where different type muscle spikes are detected, estimated and subtracted, it has been reported that the epileptic spikes if any, can be detected by template matching (matched filtering). In applying the proposed filtering also, if the record is suspected to be an epileptic one, the epileptic spikes can be detected and estimated by template matching (or by any other method) prior to filtering. Perhaps the same approach holds valid with other methods also. filtered
Acknowledgemenf.s---The authors thank The Chairman, Department of E.C.E., for providing the facilities required to carry out this research work. The first author thanks the University Grants Commission, Government of India, for the award of the research fellowship during the course of this work. They are grateful to Dr G. N. Narayana Reddy, The Director; Professor Gowri Devi, The Head, Neuro Centre and the EEG recording staff of the National Institute of Mental Health and Neuro Sciences, Bangalore, India, for their permission and help in recording the EEG data.
LMS adaptive
predictive
filtering
29
REFERENCES 1. A. Isaksson, A. Wennberg 2. 3. 4. 5. 6. 7. 8. 9. 10. II. 12. 13. 14. 15. 16.
17.
and L. H. Zetterberg,
Computer analysis of EEG signals with parametric models. Proc. IEEE 69. 451461 (1981). A.‘Isaksson and Al Wennberg, Visual evaluation and computer analysis of the EEG-a comparison. Electroencephalo. Clin. Neurophysiol. 38, 79-86 (1975). R. D. O’Donnell, J. Berkhout and W. R. Adey, Contamination of scalp muscles. Electroencephalo. Clin. Neurophysioi. 37, 145-151 (1974). L. G. Kiloh, A. J. MC Comas and J. W. Osselton, Clinical Electroencephalo., Chap. 3, pp. 4649. Butterworths, London (1979). J. S. Barlow, EMG artifact minimisation during clinical recordings by special analog filters, Electroencephalo. Clin. Neurophysiol. 58, 16 I- 174( 1984). T. L. Johnson, S. C. Wright and A. Segall, Filtering of muscle artifact from the electroencephalogram. IEEE Trans. Bio medical Engng BME-26, 56-563 (1979). F. Bartoli and S. Cerutti, An optimal linear filter for the reduction of muscle noise superimposed to the EEG signal. J. Biomedical Engng 5, 274280 (1983). J. S. Barlow, Muscle spike artifact minimisation in EEG by time domain filtering. Electroencephalo. C/in. Neurophysiol. 55, 487491 (1983). B. Widrow, J. R. Glover, J. M. McCool, J. Kaunitz, C. S. Williams, R. H. Hearn, J. R. Ziidler, E. Dong and R. C. Goodlin, Adaptive noise cancelling: principles and applications. Proc. IEEE 63, 169221716 (1975). A. V. Oppenheim and R. W. Schafer, Digital Signal Processing. Prentice-Hall, New York (1975). A. Budak, Passive and Active Network Analysis and Synthesis, Chap. 22. Houghton Mifflin Company, Boston (1974). E. H. Satorius, J. R. Ziddler and S. T. Alexander, Noise cancellation via linear prediction filtering. Int. Co& Acousfics Speech and Signal Processing, 937-940 (1979). M. R. Sambur, Adaptive noise cancelling for speech signals. IEEE Trans. Acoustics Speech and Signal Processing 26, 419423 (1978). L. J. Griffiths, A continuously adaptive lattice filter implemented as a lattice structure. IEEE Con! Acoustics Speech and Signal Processing, pp. 683-686 (1977). J. P. Burg, Maximum entropy special analysis. Ph.D. dissertation, Stanford Univ., Calif. (U.S.A.). L. H. Zetterberg, Experience with analysis and simulation of EEG signals with parametric description of spectra. Automation of Clinical Electroencephalography (Conference Proceedings, Edited by P. Kellaway and I. Petersen), pp. 161-201. Raven Press, New York (1973). A. S. Gevins, C. L. Yeager, S. L. Diamond, J. P. Spire, G. M. Zeitlin and A. H. Gevins, Automated analysis of electrical acitivty of the human brain (EEG): a progress report, Proc. IEEE 63, 1382-1399 (1975).
AUTHORS’
BIOGRAPHIES
S. V. Narasimhnn was born in Vijayapura, Bangalore (district), India in 1949. He received his B.Sc. from Bangalore University in 1969, B.E. (Elect. Communication Engng) from Indian Institute of Science, Bangalore, India in 1973; M. Tech. (Communication and Radar) from Indian Institute of Technology, Madras, India in 1977; and Ph.D. degree from Indian Institute of Science, in 1988. He was Engineer Scientist at Indian satellite centre, Bangalore, Scientist at National Aeronautical Laboratory, Bangalore, Research Associate at the Department of E.C.E., Indian Institute of Science, Post-Doctoral Fellow at the Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada; during 1973-75, 1977-83, December 19888May 1989 and May 1989-April 1991, respectively. Since June 1991, he has been working as Assistant Director in the Aerospace Electronics Division, National Laboratories, Bangalore, India. His research interests are: parametric spectral modeling, adaptive signal processing, coherence function estimation, time-frequency signal representation, group delay processing, higher order spectra, wavelet transform, EEG signal processing, speech coding, channel equalization and cockpit recorded speech processing.
30
S. V. Narasimhan
and D. Narayana
Dutt
Dutt obtained his B.E. degree from Bangalore University and M.E. and Ph.D. from the Indian Institute of Science, Bangalore, India. He is presently working in the same Institute in the Department of Electrical Communication Engineering as an Associate Professor. He had worked earlier in the areas of acoustics and speech signal processing. At present he is working in the area of applications of digital signal processing to analysis of biomedical signals; in particular, brain signals. D. Narayana
0045-7906(95)ooo30-5
Computers Elect. Engng Vol. 22, No. 1, pp. 13-30, 1996 Copyright 0 1996 Elsevier Science Ltd Printed in Great Britain. All rights reserved 00457906/96 %/5.00 + 0.00
APPLICATION OF LMS ADAPTIVE PREDICTIVE FILTERING FOR MUSCLE ARTIFACT (NOISE) CANCELLATION FROM EEG SIGNALS S. V. NARASIMHANt Department
of Electrical
and D. NARAYANA
Communication Engineering, Bangalore 560012, India
Indian
DUTTf
Institute
of Science.
(Received for publication 4 October 1995) Abstract-The presence of muscle artifact (noise) affects the electroencephalograph (EEG) analysis. This paper deals with the filtering of the muscle artifact (noise) from a muscle artifact contaminated EEG, by a hybrid approach. In this, the muscle artifact component outside the EEG band is removed by lowpass filtering and the component within the EEG band by the least mean square gradient adaptive predictive filtering. Further, the effect of the muscle artifact on the parametric representation of EEG and the improvement achieved by the proposed filtering, are considered for simulated and real EEG data. The results indicate that the proposed filtering facilitates a reasonably valid parametric representation of EEG even when it is contaminated with the muscle artifact. The adaptive predictors realized by tapped delay line and lattice structures have been considered. Key words: Least mean square adaptive cancellation.
algorithm,
predictive
filtering, EEG analysis,
muscle artifact,
noise
1. INTRODUCTION Electroencephalograph (EEG), the manifestation of the brain’s function, comprises of the widesense stationary stochastic component known as the background activity [viz. 6 (1-3 Hz), 0 (47 Hz), c( (8-13 Hz) and fl (14-30 Hz) activities] and the nonstationary paraxysmals (spikes and spike-andwaves) which are considered to be superimposed on the stationary component. Further, often an EEG registration is contaminated with extracerebral signals called artifacts and these may be due to muscle activity, ECG pickup, eyemovement, improper electrode contact, etc. These extracerebral signals play the role of noise for EEG background activity. Though in visual analysis, these artifacts are detected and the EEG is interpreted with some difficulty, they pose a major problem for the computer analysis of EEG. Of the many artifacts mentioned above, the muscle artifact is very common and may be many times larger in magnitude than the EEG signal. This artifact is due to contraction of neck and scalp muscles. Further, in a tense subject, the muscle activity is often wide spread though maximal in temporal regions. Artifacts in general and muscle artifact in particular, affect both time domain analysis, such as correlation or slope descriptors which are very sensitive to noise [l], and the frequency domain analysis (spectral analysis), by changing the spectral parameters like bandwidth, percentage of power distribution [2]. The muscle artifact is found to affect the EEG spectrum above 14 Hz considerably [3], i.e. the B-activity. In visual analysis, an attempt is often made to reduce the muscle artifact by decreasing the higher cutoff frequency of the recording amplifiers [4]. Hence, in general and in particular for automation of clinical EEG, removal or minimization of muscle artifact from EEG is mandatory. For this purpose, techniques such as analog filtering [5], nonlinear filtering 161,Kalman filtering [7] and time domain filtering [8] have been reported. However, the most common and simple least mean square (LMS) gradient adaptive algorithm which has been extensively used for noise cancelling applications in fields like speech, ECG, etc. [9], has not been used for muscle artifact cancellation from EEG. In this paper, the LMS adaptive predictive filtering has been proposed for muscle noise cancellation from EEG. The muscle noise cancellation is achieved by a hybrid approach which uses TPresent address: Aerospace Electronics Division, National $To whom all correspondence should be addressed.
Aerospace
I3
Laboratories,
Bangalore
560017, India.
14
S. V. Narasimhan
and D. Narayana
Dutt
lowpass filtering and adaptive predictive filtering. Further, the effect of muscle artifact on the parametric representation of EEG and the improvement achieved by the proposed filtering to this end are brought out. The results with simulated and real EEG indicate that the proposed filtering is very effective in reducing the muscle artifact and as a result the improvement achieved in the parametric representation of the EEG is of significance. The gradient adaptive predictive filtering has been realized both by tapped delay line (TDL) and lattice structures. In the following sections, Section 2 deals with the muscle artifact, Section 3 with the proposed method of filtering of muscle artifact from EEG background activity and Section 4 with the application of the proposed filtering and the results obtained for simulated and real EEG. _. 7 THE
MUSCLE
ARTIFACT
The muscle artifact is the result of superposition of large number of action potentials and each action potential forms a muscle spike. The simulation of muscle artifact is based on the model used by Johnson et al. [6] and the muscle spike is assumed to be an impulse response of a second order linear system given by
where h = ( 1/2&)exp(
- 27raT )sin(2&
T)
LI, = 2 exp( - 27coT )cos(2r& T ) ii: = exp( -- 47-0~7‘) T is the sampling period, o is the half bandwidth andf, is the frequency of the spectral peak. For a typical muscle spike, the second order system has a centre frequency at 70 Hz and a bandwidth 70 Hz. In the muscle artifact, the amplitude and duration of the spikes vary much more than the shape of the spike. The variability in the duration of the spikes can be achieved by time scaled version of the typical muscle spike and the scale factors can be 1.5 and 2.0. The different types of spikes occur with equal probability and the average rate of occurrence of the muscle spike has been found to be approx. 50 spikes per second. This is realized by driving the linear systems by independent Poisson noise generators having approximately one third the average rate (i.e. 15 spikes/s). 3.
FILTERING
OF
THE
MUSCLE
ARTIFACT
FROM
EEG
The spectra of the muscle artifact and the EEG background activity have maximum overlap around 30 Hz [6] and the muscle spectrum extends upto folding frequency. Hence, the muscle artifact can be considered to be made up of two components: (1) the out of band component (outside the EEG frequency band); and (2) the inband component (within the EEG frequency band). In the proposed method of filtering of muscle artifact, the out of band component is removed by a lowpass filter and subsequently the inband component by an adaptive filter. The out of band component is removed by a fourth order Butterworth lowpass filter having cutoff frequency at 35 Hz. This is based on the fact that EEG background activity spectrum is confined to 30 Hz [l]. This lowpass filter has a fairly linear phase characteristic at least within the EEG band of interest. In this study, an equivalent digital infinite impulse response filter [lo] is considered. In practice, a fourth order Butterworth active lowpass filter can be used either in addition to the antialiasing filter or as an antialiasing filter itself. Further, any phase distortion introduced by it due to small amount of non-linearity in its phase characteristic can be compensated by an equalizer [I 11. After lowpass filtering, the inband muscle artifact component is removed by least mean square adaptive predictive filtering. Such a filtering is possible, provided the signal bandwidth is significantly less than the bandwidth of the additive noise [12]. The inband component of the muscle artifact plays the role of the wide band noise and the EEG plays the role of a narrow band signal. The linear prediction filter schematic is shown in Fig. 1. The linear prediction filter coefficients are
LMS adaptive predictive filtering
(4 g 0 0
r
0 0 0 :: c
::
0 COO Or‘ ”
x0
0
&
30 +cw JO ii? CfO 0
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0.01
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0 -03 IN SECS
Fig. 1 (a) and (b)-cupkon
CAEE *2,1-a
0.04
ooerleaf.
0.05
0.06
S. V. Narasimhan
16
(c)
and D. Narayana
Dutt
8 0 0
“,? =0 t-t.
7 ‘0.00
0.50
1
.oa
1 .50 TIME
(
‘0 -00
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2 IN
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3
00
3
50
4.00
SECS
“‘; “‘\yyy
160.00 240.00 FRQ IN HZ
320.00
400.00
480.00
Fig. I (a) Impulse response of tilters generating different types of muscle spikes with bandwidths: 70 Hz+O); 46.67 Hz+A) and 35 Hz-( x ). (b) Logmagnitude spectra of the impulse response of filters generating the muscle spikes with bandwidths: 70 Hz--(l); 46.67 HZ-(~) and 35 HZ-(~). (c) Simulated muscle artifact (sampled at 200 Hz). (d) Spectral density of the simulated muscle artifact (sampled at I kHz).
chosen such that the power of the error signal ~(a) between the predictor output (estimate) a(n) and the actual signal x(n), is minimized. The delay D represents the prediction distance of the filter. The noise suppression is due to the fact that the decorrelation time for broad band noise is much smaller than that of a narrow band signal. This enables the choice of the value of D which will effectively decorrelates the broad band noise and also prevents the noise appearing in the predictor output. To explain in physical terms, due to high correlation, prediction of an EEG signal sample from its previous samples is valid. Whereas, such a prediction is not valid for muscle artifact, as it is a very rapidly changing signal (poor correlation). An adaptive version of the linear prediction filter has been successfully used for processing noisy speech [13]. In the present study, the LMSTDL and LMS-lattice gradient adaptive predictors are applied for muscle artifact removal. A brief description of the two predictors from the point of view of noise cancellation is given below.
LMS adaptive predictive filtering
3.1. The LMS-TDL
17
predictor
Figure l(a) itself forms the schematic of the LMSTDL predictor. The estimate, Z(n) of the present input sample x(n) is based on the P previous input samples which are D samples away from the present instant [13,14]. That is c?(n) = i
-k
a,x(n
- D + 1).
(2)
k=l
By LMS gradient adaptation
rule,
&(n) = &(fi - 1) + 2p&(n -
1)X@
-D),
-k
k=l,2,...,P
(3)
where E(n) =x(n)
-i(n),
i ---O<<
< y < 1.
y is the forgetting factor. This is the commonly used LMS-TDL algorithm. A variation in the definition of p is given by, p = q/E,(n), 0 < q < 1, where q = i/P (i.e. in the sense 4 is sufficiently small) and the division of q by E, takes care of the nonstationary nature of the input. This definition of p is used in the present study. 3.2. The LMS-lattice
predictor
Figure l(b) shows the basic lattice filter. f,(n) and b,(n) are the forward and backward prediction errors at stage m and at time instant n. K,(n) is the reflection coefficient at stage m and instant n. The decorrelation delay D is introduced in the backward error path [14]. The lattice relations are given by J,(n) =x(n),
b,(n - 1) = x(n -D);
fm(n)=fm-,(n)+K,(n)b,-,(n Un)=~,-,(n
- 1)9
- l)+Kz(nK-1(n)
and jK,(n)] < 1, m = 1,2,3, . . . , P, for all n. The adaptation
rule for the reflection coefficient K,,,(n) is given by
K& + 1)= &l(n) - %l(n + 1)
a-L
aKm(n)
(4)
where J%,= [f;(n)
&n(n)=
+ b%)1/2.0,
v
,o
J%,-,(n - 1)
and E,(n)
= yE,(n - 1) + [f:(n)
+ b;(n
- l)],O < y < 1.
The coefficients in equation (2) are computed from the reflection coefficients K,,,(n), using Levinson’s recursion relation [15] and equation (2) gives the predictor output.
S. V Narasimhan
and D. Narayana
Dutt
---_____-1.
Adaptive
alOor
ithm
(a)
f, Cnl
f, (nl
L- ~~.._~._.._..__
__
Fig. 2. (a) The LMS -TDL
4.
._
predictor
f,*
*
( _I
predictor
with
_l?! _-.. ._ with decorrelation delay. decorrelation delay.
APPLlCATION
AND
The performance of the LMS-TDL and LMSlattice muscle artifact from muscle artifact contaminated EEG, presented here. 4.1. Studies with simulated
f (Ill
(n)
(b) The LMS
lattice
RESULTS adaptive predictors both for simulated
for the removal of and real signals are
data
The required muscle artifact is generated by filtering independent Poisson random number sequences by transfer functions corresponding to different types of spikes and adding the filter outputs with appropriate gain factors. As already mentioned, the three types of spikes, each characterized by the transfer function (1) and realized by bandwidths 70, 46.67 and 35 Hz and center frequency 70 Hz (in each case) are considered. Their corresponding impulse responses and the frequency responses are shown in Figs 2(a) and (b). The sampling rate used is I KHz. The muscle artifact thus generated and its spectral density function are shown in Figs 2(c) and (d), respectively. This muscle data [Fig. 2(c)] will be added to EEG data to get muscle artifact contaminated EEG. For the purpose of illustrating the removal of muscle noise, a typical EEG signal having specifications given in Table 1 [16] and sampled at frequency 200 Hz is considered. Further, the muscle noise generated is down sampled to 200 Hz and added to the EEG data. The choice of this sampling frequency is based on the studies carried out by Bartoli and Cerutti [7] and Gevins et al. [17]. The EEG to muscle artifact power ratio is - 2.48 dB. The EEG signal and the muscle artifact contaminated EEG are shown in Figs 3(a) and (c), respectively. Their corresponding spectra are shown in Figs 4(a) and (b). The original EEG signal itself is filtered by the Butterworth lowpass Table Activity il R 6
I.
Stxcitication
of the simulated
EEG s~anal
0 (Hz)
/1 (Hz)
% of power
0.58 I.36
10.25 1891) 0.00
63 4 33
I .27
(D is the half bandwidth for a and fl-activities and full bandwidth for ~5-activity. .& is the centre frequency)
LMS adaptive
predictive
filtering
19
(a)
u
.
=)o ‘N
5 ‘0.00
0
50
I
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@z
1 .50 TlflE IN
2 .a0
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SECS
(b)
5 -2
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00
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0:
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.50
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IN
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(e)
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(f)
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-e
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2.00 SECS
Fig. 3. (a) The simulated EEG signal. (b) the lowpass filtered simulated EEG signal, (c) the muscle artifact contaminated EEG, (d) the lowpass filtered muscle artifact contaminated EEG, (e) the LMSTDL predictor output, (f) the LMS-lattice predictor output.
filter in order to assess its effect on the signal. It is evident that the filtered and original signals are almost identical except for the small time shift introduced by the lowpass filter [Figs 3(a) and (b)]. Their spectra are also identical within the EEG band [Fig. 4(a)]. The muscle artifact contaminated EEG is filtered by the lowpass filter. The lowpass filtered signal is only free from the out of band EEG muscle noise component but not completely free from the muscle artifact, and the remaining noise is due to inband component [Figs 3(d) and 4(b)]. By subjecting the lowpass filtered output to adaptive filtering, the remaining noise is removed. The order of the adaptive filter P used is 10 and the decorrelation delay D is 20 samples. The values of c and y used are 0.2 and 0.92, respectively. Further, q = v = C/P. Figures 3(e) and 3(f)
S. V. Narasimhan
20
:o.oo
B-00
16
FRW
FRQ
00
IN
JN
and D. Narayana
24
HZ
00
32
.oo
40
oc
100.00
Hil
Fig. 4. (a) Logmagnitude spectra for: EEG signal-(O); spectra for: muscle artifact contaminated EEG--(O), EEG-(A).
Dutt
lowpass lowpass
filtered filtered
48
.co
120.00
EEG-_(A). (b) Logmagnitude muscle
artifact
contaminated
indicate that the adaptive filters will converge only after 0.25 s (approximately). After convergence, the adaptive filtered signals have close resemblance in their nature to the original EEG [Fig. 3(b)]. The LMS-lattice output appears to have somewhat more noise than the output of LMS-TDL. In practice, often such a convergence time will be small as the predictive filter would have originally adapted to the existing EEG data and only the muscle noise superimposes suddenly on it. In this study, in order to find the effect of the muscle artifact on the parametric representation of EEG and the improvement achieved by the proposed filtering; original EEG, the muscle artifact
LMS adaptive predictive filtering
21
contaminated EEG, the lowpass filtered signal and the adaptive filtered outputs are subjected to Burg’s spectral estimation [15]. For this purpose, a 15th order prediction filter has been used and to ensure convergence of adaptive filter outputs, in the signal sequence, later 512 samples are considered leaving the initial 240 samples. For the signals at other stages of filtering, the corresponding data block of 512 points are used. Figure 5 shows the respective Burg’s spectra. It is seen that for the muscle artifact contaminated EEG signal, the Burg’s spectrum does not indicate
g
3
2
lo. I
*0”
\
‘0 .oo
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_-_
-__
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32.00
40.00
48.00
32.00
40.00
4b.00
(b)
.OCI
'fRQ
IN
24.00 HZ
Fig. 5. (a) Burg spectra for: lowpass filtered simulated EEG-Q), muscle artifact contaminated EEG--(2), lowpass filtered muscle artifact contaminated EEG-(3), LMS-TDL predictor output-(4). (b) Burg spectra for: lowpass filtered simulated EEG-(I), muscle artifact contaminated EEG-(2), lowpass filtered muscle artifact contaminated EEG-(3), LMS-lattice predictor output-+4).
22
S Table 2. Estimated
V.
Narasimhan and D. Narayana Dutt
frequency by Burg’s method of spectral estimatmn of filtering (for simulated EEG)
S1gKIls
(I) (2) (3) (4) (5)
for signals at different stages
a-activity peak frequency (Hz)
/J-activity peak frequency (Hz)
10.742 IO.547 IO.156 IO.352 10.742
20.1 I7 ml 20.308 19 726 IY 922
EEG (Original) EEG + Muscle artifact Lowpass filtered (EEG + Muscle amfact) LMS-TDL predictor output LMS-lattice predictor output
the peak corresponding to j-activity at all. For the lowpass filtered signal, though there is a peak corresponding to P-activity. this peak is almost at the same level as the noise peak. But after subsequent adaptive filtering, all the peaks are clearly brought out by the Burg’s method and the p-activity peak is much above the noise level. This indicates that the Burg’s parametric representation severely gets affected by the muscle noise and the proposed filtering method alleviates this problem. The performance of the two adaptive predictors are almost same. However, for the LMS-lattice predictor output, the noise level is somewhat higher than that of the LMS-TDL. This is in agreement with the time domain observation [Figs 3(e) and 3(f)]. The frequencies estimated by Burg’s method at different stages of filtering are listed in Table 2. The accuracy of peak frequency estimation of a and p-activity peaks is approximately same for lowpass filtered output and for both the predictor outputs. However, prior to any filtering, the Burg’s spectral estimation does not indicate a-acitivty peak and hence its frequency. In the above illustration, the Burg’s spectrum of the lowpass filtered EEG signal [Fig. 3(b)] is taken as reference for comparison instead of the Burg’s spectrum of the original EEG. This is due to the fact that the Burg’s spectrum for filtered EEG signal and for the original EEG will be different as the method is very sensitive to noise. The original EEG signal, which is simulated by filtering the white Gaussian noise by filters occupying different frequency bands of the EEG spectrum will have some noise up to the folding frequency. But in the lowpass filtered signal this noise is removed. This reference for comparison is valid as the lowpass filtered EEG and the original EEG are identical within the EEG band [Fig. 4(a)]. Further, since for the lowpass filtered simulated in the spectrum
!a)
FJ 00 FRO
IN
16 00 HI
24 .oo
32.
00
(b)
4c .OO
‘0 .oo
B .oo FRQ
16.00 N HZ
24 .oo
32.00
Fig. 6. (a) Logmagnitude spectrum for 1Sth order LMS-TDL predictor output (simulated EEG). Spectra for: (O)-lowpass filtered EEG; (A)-10th order predictor output; (x )-15th order predictor output. (b) Logmagnitude spectrum for 15th order LMS-lattice predictor output (simulated EEG). Spectra for: (O)-lowpass filtered EEG, (A)-10th order predictor output; ( x )--15th order predictor output.
40 .oo
LMS adaptive predictive filtering
23
EEG, the Burg’s method has been found to provide a better spectral estimate (in particular, the B-activity spectral peak) with a 15th order than with a 10th order, the 15th order has been used for performance evaluation though the adaptive predictor used is of 10th order. The effect of higher decorrelation delay D on the performance of the adaptive predictors has also been assessed. A higher delay will result in a decrease on the magnitude of the /?-acitivty peak in the spectrum of the adaptive filtered signal. This has been verified for a delay of 30 sample points with a 15th order prediction filter. For LMSTDL predictor, the decrease in p-activity spectral peak magnitude is significant compared to that of LMS-lattice [Figs 6(a) and (b)].
JIflE
1N
SECS
:t
z=: 752 ‘0.00
,g
0.50
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Fig 7. (a) Muscle artifact contaminated real EEG segment; (b) the lowpass filtered muscle artifact contaminated EEG; (c) the LMS-TDL predictor output; (d) the LMSlattice predictor output; (e) Burg’s spectra for: Muscle artifact contaminated real EEG-_(I), lowpass filtered muscle artifact contaminated EEG-(2). LMS-TDL predictor output-(3). LMSlattice predictor output-(4).
The adaptive predictors though bring out the p-activity peak well above the noise level, they seem to slightly decrease the S-activity peak magnitude (Figs 5 and 6). 4.2. Studies with real EEG data The real EEG data has been recorded using a Schlumberger 5521/2) with a tape speed of (I S/S) in/s. Further, the analog
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digital conversion) by a RIP-1 1 based hybrid computer and the digital data is stored in a magnetic tape. The digitization is done at a sampling rate of 200 Hz using a 13 bit (12 bits + 1 sign bit) analog to digital converter. For the purpose of illustration, a data segment of an alcoholic patient of age 53 yr recorded from Fpl-Fp2 electrode pair with the high frequency cut off at 120 Hz on the EEG machine, is considered. This case is particularly chosen with an intention that the EEG activity will contain predominantly the B-activity and it is this activity that gets affected by the muscle noise to a greater extent than a and &activities.
S. V. Narasimhan and D. Narayana Dutt
26
Figure 7(a) shows the real EEG signal for which the mean is removed. This is filtered by the 4th order Butterworth lowpass filter and the filtered signal is shown in Fig. 7(b). The outputs of the adaptive filters are shown in Figs 7(c) and 7(d) when the lowpass filter output is fed as input to them. The adaptive filters have the same parameters as in simulation study. Figure 7(e) shows the Burg’s spectra for the adaptive filter outputs, It also incorporates the spectra for the original EEG (mean removed) and that for the lowpass filtered signal, for comparison. In computing the Burg’s spectra as in simulation study, the later 512 data samples are considered leaving the initial 240 samples in each case.
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Fig. 9. (a) Muscle artifact contaminated real EEG segment; (b) the lowpass filtered muscle artifact contaminated EEG; (c) the LMS-TDL predictor output; (d) the LMS-lattice predictor output; (e) Burg’s spectra for signals shown in Fig. 9(a)--(I), Fig. 9(b)-(2), Fig. 9(c)--(3); (f) Burg’s spectra for signals shown in Fig. 9(a)--(l), Fig. 9(b)--(2), Fig. 9(d)--(3).
It is seen that the adaptive filters are very effective in removing the muscle noise and the lowpass filter alone is not sufficient [Figs 7(a)-(d)]. Also, in the Burg spectrum for the muscle artifact contaminated EEG [Fig. 7(e)], the /?-activity peak is not brought out clearly and it is almost at noise level and the peak occurs at 22.65 Hz. With lowpass filtering, though the /?-activity peak is brought out, it is associated with additional peaks of approximately the same magnitude and are due to inband muscle noise. These peaks occur at 19.53, 26.76 and 36.32 Hz. But for the adaptive
28
S. V. Narasimhan
and D. Narayana
Dutt
signals, the p-activity peak is brought out clearly and the additional peaks due to inband muscle noise are reduced significantly and only one peak is detected by the peak and LMS-lattice. picking algorithm. This peak occurs at 20.31 and 19.14 Hz for LMS-TDL respectively. The general applicability of the proposed method has been ascertained by considering other EEG data segments (of different subjects) and the results obtained (Figs 8 and 9) are found to be in agreement with the above illustration of real EEG data. It is important to note that with real EEG signals, there is no appreciable decrease in d-activity due to adaptive filtering, unlike that with the simulated EEG. In evaluating the performance of the proposed filtering, the Burg’s spectral estimator is used as reference and this is relevant due to the fact that the sequential adaptive predictors and the Burg’s spectral estimator are based on the same model. viz. the all-pole model and further, the latter provides an optimum estimate for the model assumed. This study clearly indicates that even in the case of real data, the parametric estimation gets affected by the muscle noise (both outside and inside EEG frequency band) and the proposed method minimizes the muscle noise significantly and enables the Burg’s parametric estimation to provide valid results. Generally. the results obtained with real data are consistent with those of simulated data at different stages of filtering. The present study has only aimed at exploring the possibility of applying the LMS predictive filtering for muscle noise cancellation. However. no efforts have been made to evaluate the comparative performance of this method in removing the muscle noise with the existing ones such as Kalman filtering [7] and non-linear filtering [6]. The study indicates that the proposed filtering provides satisfactory results from the point of view of parametric spectral estimation and is computationally efficient compared to other methods mentioned. In particular. it is quite satisfactory in bringing out the {j-activity spectral peak above the noise level in the Burg’s spectrum. This is of significance, since. it is the /f-activity that gets affected by the muscle artifact to the maximum extent. The LMSTDL and the LMS-lattice provide almost same performance. Of the two. LMS -TDL is computationally simpler and hence can be preferred to lattice. In the literature. the performance of the existing methods of muscle noise cancellation from EEG (mentioned), have not been assessed from the point of view of parametric spectral modeling. The present study establishes that the proposed filtering provides reasonably valid results even from this point of view and this is an additional information about the utility of sequential adaptive algorithms for muscle noise cancellation. Further, from the spectral estimation point of view, the study emphasises that mere lowpass filtering [2] is not sufficient and some type of adaptive filtering is essential to remove the muscle noise component within the EEG frequency band. In the present study. though the proposed filtering has been used for muscle noise cancellation from EEG background activity. a question may arise is that whether this filtering can distinguish between a muscle spike and an epileptic spike. Certainly, this distinction is not possible, since, any linear filtering is blind to different types of spikes and so is the proposed filtering. The muscle noise cancellation methods mentioned [5-81 also have this problem, However, with the nonlinear filtering [8]. where different type muscle spikes are detected, estimated and subtracted, it has been reported that the epileptic spikes if any, can be detected by template matching (matched filtering). In applying the proposed filtering also, if the record is suspected to be an epileptic one, the epileptic spikes can be detected and estimated by template matching (or by any other method) prior to filtering. Perhaps the same approach holds valid with other methods also. filtered
Acknowledgemenf.s---The authors thank The Chairman, Department of E.C.E., for providing the facilities required to carry out this research work. The first author thanks the University Grants Commission, Government of India, for the award of the research fellowship during the course of this work. They are grateful to Dr G. N. Narayana Reddy, The Director; Professor Gowri Devi, The Head, Neuro Centre and the EEG recording staff of the National Institute of Mental Health and Neuro Sciences, Bangalore, India, for their permission and help in recording the EEG data.
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REFERENCES 1. A. Isaksson, A. Wennberg 2. 3. 4. 5. 6. 7. 8. 9. 10. II. 12. 13. 14. 15. 16.
17.
and L. H. Zetterberg,
Computer analysis of EEG signals with parametric models. Proc. IEEE 69. 451461 (1981). A.‘Isaksson and Al Wennberg, Visual evaluation and computer analysis of the EEG-a comparison. Electroencephalo. Clin. Neurophysiol. 38, 79-86 (1975). R. D. O’Donnell, J. Berkhout and W. R. Adey, Contamination of scalp muscles. Electroencephalo. Clin. Neurophysioi. 37, 145-151 (1974). L. G. Kiloh, A. J. MC Comas and J. W. Osselton, Clinical Electroencephalo., Chap. 3, pp. 4649. Butterworths, London (1979). J. S. Barlow, EMG artifact minimisation during clinical recordings by special analog filters, Electroencephalo. Clin. Neurophysiol. 58, 16 I- 174( 1984). T. L. Johnson, S. C. Wright and A. Segall, Filtering of muscle artifact from the electroencephalogram. IEEE Trans. Bio medical Engng BME-26, 56-563 (1979). F. Bartoli and S. Cerutti, An optimal linear filter for the reduction of muscle noise superimposed to the EEG signal. J. Biomedical Engng 5, 274280 (1983). J. S. Barlow, Muscle spike artifact minimisation in EEG by time domain filtering. Electroencephalo. C/in. Neurophysiol. 55, 487491 (1983). B. Widrow, J. R. Glover, J. M. McCool, J. Kaunitz, C. S. Williams, R. H. Hearn, J. R. Ziidler, E. Dong and R. C. Goodlin, Adaptive noise cancelling: principles and applications. Proc. IEEE 63, 169221716 (1975). A. V. Oppenheim and R. W. Schafer, Digital Signal Processing. Prentice-Hall, New York (1975). A. Budak, Passive and Active Network Analysis and Synthesis, Chap. 22. Houghton Mifflin Company, Boston (1974). E. H. Satorius, J. R. Ziddler and S. T. Alexander, Noise cancellation via linear prediction filtering. Int. Co& Acousfics Speech and Signal Processing, 937-940 (1979). M. R. Sambur, Adaptive noise cancelling for speech signals. IEEE Trans. Acoustics Speech and Signal Processing 26, 419423 (1978). L. J. Griffiths, A continuously adaptive lattice filter implemented as a lattice structure. IEEE Con! Acoustics Speech and Signal Processing, pp. 683-686 (1977). J. P. Burg, Maximum entropy special analysis. Ph.D. dissertation, Stanford Univ., Calif. (U.S.A.). L. H. Zetterberg, Experience with analysis and simulation of EEG signals with parametric description of spectra. Automation of Clinical Electroencephalography (Conference Proceedings, Edited by P. Kellaway and I. Petersen), pp. 161-201. Raven Press, New York (1973). A. S. Gevins, C. L. Yeager, S. L. Diamond, J. P. Spire, G. M. Zeitlin and A. H. Gevins, Automated analysis of electrical acitivty of the human brain (EEG): a progress report, Proc. IEEE 63, 1382-1399 (1975).
AUTHORS’
BIOGRAPHIES
S. V. Narasimhnn was born in Vijayapura, Bangalore (district), India in 1949. He received his B.Sc. from Bangalore University in 1969, B.E. (Elect. Communication Engng) from Indian Institute of Science, Bangalore, India in 1973; M. Tech. (Communication and Radar) from Indian Institute of Technology, Madras, India in 1977; and Ph.D. degree from Indian Institute of Science, in 1988. He was Engineer Scientist at Indian satellite centre, Bangalore, Scientist at National Aeronautical Laboratory, Bangalore, Research Associate at the Department of E.C.E., Indian Institute of Science, Post-Doctoral Fellow at the Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada; during 1973-75, 1977-83, December 19888May 1989 and May 1989-April 1991, respectively. Since June 1991, he has been working as Assistant Director in the Aerospace Electronics Division, National Laboratories, Bangalore, India. His research interests are: parametric spectral modeling, adaptive signal processing, coherence function estimation, time-frequency signal representation, group delay processing, higher order spectra, wavelet transform, EEG signal processing, speech coding, channel equalization and cockpit recorded speech processing.
30
S. V. Narasimhan
and D. Narayana
Dutt
Dutt obtained his B.E. degree from Bangalore University and M.E. and Ph.D. from the Indian Institute of Science, Bangalore, India. He is presently working in the same Institute in the Department of Electrical Communication Engineering as an Associate Professor. He had worked earlier in the areas of acoustics and speech signal processing. At present he is working in the area of applications of digital signal processing to analysis of biomedical signals; in particular, brain signals. D. Narayana