Correction of muscle artefacts in the EEG power spectrum

Clinical Neurophysiology 116 (2005) 2044–2050 www.elsevier.com/locate/clinph

Correction of muscle artefacts in the EEG power spectrum Theo Gassera,*, Jan C. Schullera, Ursula Schreiter Gasserb a

Department Biostatistics, Institute for Social and Preventive Medicine, University of Zurich, Sumatrastr. 30, CH-8006 Zu¨rich, Switzerland b Gerontopsychiatrisches Zentrum Hegibach, Minervastr. 145, CH-8029 Zu¨rich, Switzerland Accepted 3 June 2005

Abstract Objective: To provide a method for correcting muscle artefacts in fast band power at EEG derivations. Methods: We define an indicator of surface EMG as power in the band 51.0–69.0 Hz (‘muscle power’). This indicator is used to approximately eliminate the contribution of muscle activity on fast band power via a regression model. Results: (1) Patients show a larger proportion of muscle activity in fast band power. (2) There is a clear topographic pattern, frontal–temporal derivations being most susceptible to EMG artefacts. (3) The contribution of surface EMG can be drastically reduced by the proposed correction method. (4) Without correction, results for fast bands can be biased when e.g. comparing control and patient groups and the proposed correction method by and large eliminates this bias. Conclusions: It is advisable to correct the quantitative EEG reflecting fast activity for the extent of EMG artefacts. Significance: To render the quantitative EEG more valid as an indicator of cerebral activity. q 2005 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. Keywords: Artefacts; EMG; Quantitative EEG; Beta band

1. Introduction The contamination of EEG recordings by artefacts is a major problem and even more so for the quantitative EEG (qEEG). Major sources are EOG artefacts due to moving eyes and/or the lids and muscle (EMG) artefacts. For EOG artefacts, correction methods for the EEG signal have been developed in the 1980’s (Brunia et al., 1989 and the literature cited there), based on understanding the transfer of EOG potentials to EEG derivations (Gasser et al., 1985). EOG artefacts affect slow EEG activity, EMG artefacts fast activity. Unfortunately, there is no source signal of EMG activity available (Barlow, 1986), comparable to vertical and horizontal EOG recordings, which can be used as predictors for EEG artefacts caused by EOG. Rejection of epochs contaminated by artefacts is an obvious solution for a quantitative analysis. To do this automatically, is a challenging task for EMG artefacts

* Corresponding author. Tel.: C41 1 634 4640. E-mail address: [email protected] (T. Gasser).

(van de Velde et al., 1998), and some artefacts may remain and more so for patients who often do not follow adequately instructions during recording. Since no source signal is available, we will present here a method to correct for the influence of EMG artefacts on EEG band power, rather than try to correct the EEG trace itself. Surface EMG activity goes down as far as 12–15 Hz and overlaps with beta-activity (Gotman et al., 1975; O’Donnell et al., 1974). Goncharova et al. (2003) report on an experimental study and demonstrate that frontalis and temporalis muscle contraction had maximum amplitude frontally from 20 to 30 Hz and temporally from 40 to 89 Hz. However, substantial EMG activity stretched well into the beta and even alpha range. Digital low-pass filtering of the EEG for removing EMG artefacts has been tried with a cutoff at 12.5 Hz or somewhat higher (Barlow, 1984; Gotman et al., 1981). Inevitably, beta activity will then be distorted. Blind source separation via independent component analysis has been suggested for eliminating artefacts in the signal itself, including muscle artefacts (Jung et al., 2000). The main idea in our approach is to take power in the band 51–69 Hz (‘muscle band’) as an indicator of EMG

1388-2457/$30.00 q 2005 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.clinph.2005.06.002

T. Gasser et al. / Clinical Neurophysiology 116 (2005) 2044–2050

activity, and to use this parameter as a predictor in a regression analysis to eliminate EMG power in fast bands at EEG sites. The choice of frequency band is pragmatic and could be changed, if necessary; it avoids including substantial EEG activity in the EMG indicator while it contains substantial EMG power for most types of surface EMG activity. Power in high frequency bands has been used before for rejecting contaminated epochs (MacGillivray and Wadbrook, 1975). We will present a method for correcting EMG artefacts in band power, will evaluate its usefulness and will show its practical relevance on a real data set with Alzheimer patients and a control group. Beta band power for contaminated epochs is expected to be higher than for non-contaminated epochs before correction and should ideally be similar after correction. The relevance of the correction will be checked by comparing muscle power in patients and controls and the effect of EMG correction on fast EEG bands (apriori one would expect more EMG artefacts for patients and thus inflated values for beta power). Along the way, we will check which derivations are most affected, frontal and anterior temporal ones being good candidates, and whether the influence of muscle activity is also noticeable in the fast alpha band.

2. Subjects and methods 2.1. Subjects Alzheimer patients (nZ44 presenile, nZ34 senile) and normal subjects (nZ66) with a similar age distribution were included in this methodological investigation. Part of the evaluation is based on subsamples of 15 presenile and 15 control subjects previously analyzed (Schreiter Gasser et al., 1993), for reasons given in 2.2. Further details will be given in a forthcoming clinically oriented manuscript.

2.3. Quantitative EEG analysis A correction for ocular artefacts was made (following Mo¨cks et al., 1989) before further analysis. A Fast Fourier Transform (FFT) was then performed on the entire epoch of approximately 20 s, representing 4096 points. Spectral power was computed in the following bands: delta 1.5–3.5 Hz, theta 3.5–7.5 Hz, alpha1 7.5–9.5 Hz, alpha2, 9.5–12.5 Hz, beta1 12.5–17.5 Hz, beta2 17.5–25.0 Hz. Power in a ‘muscle band’ 51.0–69.0 Hz has also been computed (to be used as an indicator for the strength of EMG contamination in the EEG). The logarithm of band power was routinely used, since it leads to approximately normally distributed variates (Gasser et al., 1982). 2.4. Statistical methods for evaluation Determining an artefact correction. In order to eliminate, the influence of muscle contamination on EEG power as much as possible, a linear regression analysis was performed with log power in the muscle band as explanatory (x) variable and log EEG band power as outcome (y) variable. The value on the regression line for the respective individual value of muscle power was then subtracted from individual log EEG band power in order to obtain log EEG band power relatively free from the influence of muscle artefacts (this is similar to perform an age-standardization for an age-dependent quantity). See also Fig. 1 below. By computing a linear regression between log muscle power and log EEG band power, we also obtain the squared correlation coefficient between these two quantities; the squared correlation coefficient provides valuable information on the relevance of muscle correction since it indicates, which percentage of sample variance in EEG band power is due to muscle power, and thus to surface EMG. Validity of muscle power as an indicator of muscle activity. For the 15 patients and 15 controls mentioned above we scored visually six epochs of 20 s of EEG at F7

2.2. Recording

3 log power beta 1 F7

The EEG was recorded at 17 locations unipolarly against linked shunted earlobes as a reference (F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2). Movements of the eye and the lid were monitored by two bipolar EOG derivations, vertical and horizontal. Recordings took place in a sound-attenuated, shielded room with a Nihon Kohden 4321 G amplifier with a time constant of 1 s and a low-pass filter at 70 Hz. Digitizing of 120 s of activity at rest, with eyes closed, was performed at a sampling frequency of 200 Hz. For further analysis an epoch of 20 s was selected according to criteria set down by Mo¨cks and Gasser (1984), supplemented by a visual screening. For the two subgroups of 15 patients and 15 controls (see above), we have the full 120 s on the computer, which allows comparisons of 6 consecutive epochs of 20 s.

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1 2 log power muscle F7

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Fig. 1. Influence of muscle band power on beta1 band power at F7 (,Z normal subjects, DZpresenile AD patients, CZsenile AD patients). Regression for normal subjects (solid line), for senile patients (dashed) and for presenile patients (dotted). Both scales: log(mV2).

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according to the extent of muscle contamination per epoch. These scores were compared with the size of muscle power (for each subject) to assess the agreement between the visually scored amount of EMG activity and ‘muscle power’ (power in the band 51.0–69.0 Hz). Validity of correction. Out of all available epochs of 20 s at derivation F7 for the 144 subjects we selected 20 epochs with negligible and 20 epochs with substantial muscle contamination. A comparison of power in the bands alpha2, beta1 and beta2 before and after correction of muscle artefacts was then done for the two samples of contaminated and uncontaminated EEG activity. Following a suggestion by a reviewer, an evaluation under controlled conditions was also performed. Out of the 30 subjects with 120 s of activity available, 10 subjects were selected who had an epoch of 20 s with substantial and one without discernible muscle artefacts. Only those portions of the first epoch affected by muscle artefacts were added to the ‘clean’ data to obtain contaminated data. By correcting contaminated fast band power for EMG artefacts, one should obtain roughly band power of the ‘clean’ data. This is a controlled experiment which is not fully realistic but close to. Relevance of correction. Muscle power was analyzed separately for the two AD patient groups and for the control group over all derivations. Then a repeated measures analysis of variance was computed to assess differences between groups (and derivations) in muscle power. EEG power in the fast bands was computed for the three groups before and after correction and submitted to repeated measures analysis of variance.

3. Results 3.1. Determining an artefact correction Fig. 1 shows the linear regression lines between power in the muscle band and beta1 power at for F7 for Alzheimer patients and controls. Each square, cross or triangle represents log band power for one subject. A strong dependence of beta power on muscle activity became evident. Different intercepts for the three groups had to be allowed, since EEG power has been reported to be different (Schreiter Gasser et al., 1993). To assess the relevance of muscle activity for fast EEG bands we computed squared correlation coefficients between muscle power and EEG band power from alpha1 to beta2 (values above 0.058/0.085/0.122 are significant at the 5% level for the three groups). Squared correlation coefficients give the proportion of EMG power present in EEG band power for the respective sample. The normal group was much less affected by EMG artefacts than the two patient groups. Fronto-temporal derivations showed a heavier contamination than central-parictal ones, and contaminations increased away from the midline. For

patients, 50% or more of beta2 power was due to muscle artefacts at sensitive derivations. The influence of surface EMG on beta1 power proved to be still large, and even for the alpha2 band far from negligible for the most contaminated derivations. 3.2. Validity of muscle power as an indicator of muscle activity Visual evaluation. For subgroups of 15 patients and 15 controls, we checked visually six periods of 20 s per subject scoring the extent of muscle activity in the EEG trace. A comparison was then made with the values obtained for power in the muscle band. For subjects showing epochs both with and without muscle artefacts high visual scores went along with large muscle power intraindividually, and low visual scores with a small amount of muscle power. Regarding the agreement over all 174 epochs evaluated, the following result was obtained. Out of those 44 epochs of 20 s with the highest values for muscle power 40 epochs were visually scored as being affected by muscle artefacts. Out of the 44 epochs with the lowest values for muscle power 40 epochs were visually scored as being free of muscle artefacts (in the four cases rated wrongly a second look showed that fast beta activity was visually mistaken for a slight contamination by muscles). A controlled experiment. Fig. 2 shows beta1 and alpha2 band power (at F7) for 10 subjects from a 20 s epoch without discernible surface EMG (named ‘true’, being the benchmark) and from an epoch where muscle artefacts had been added to the same data to give contaminated data and band power (above). The band power corrected for EMG artefacts was much improved for all subjects and close to the benchmark ‘true’ (the linear function indicates a perfect agreement between ‘true’ and corrected band power). Spectral distribution. For one of the visually evaluated subjects we computed log power spectra for one epoch of 20 s of EEG with and one epoch without contamination by muscle activity. One notes that the fast frequency bands were much elevated for the contaminated epoch and this extended to a diminishing extent well into the beta frequency range (Fig. 3). 3.3. Validity of correction Correction of contaminated and uncontaminated epochs. After visually selecting 20 epochs of 20 s without muscle activity and 20 epochs with substantial muscle activity (across all 144 subjects), we computed log band power for the beta 2 band with and without correction. Fig. 3 shows box plots for this analysis. While beta2 power was much elevated for contaminated data, a close match became evident after correction. Further, variability for the contaminated data decreased when correcting for the extent of muscle artefacts. The derivation F7 was chosen, since it

T. Gasser et al. / Clinical Neurophysiology 116 (2005) 2044–2050

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Fig. 2. Log fast band power for 10 subjects for uncontaminated epochs (‘true’ on x-scale) and for contaminated epochs (contaminated on y-scale above), and after correction below (‘corrected’ on y-scale).

contains substantial muscle artefacts. Other derivations show, however, qualitatively similar results (compare Figs. 4 and 5 below). 3.4. Relevance of correction Fig. 5 shows average muscle power for presenile and senile AD patients and controls across derivations. Log muscle power was stronger for patients, and these differences were statistically highly significant. No topographic differences came up between groups. Muscle activity was minimal at the midline and increased steeply with increasing distance from the midline.

AD patients had lower beta power than controls, but when applying a correction of muscle artefacts, these differences became much more accentuated. Fig. 6 shows this for beta2 power. While the senile patients were relatively close to controls before correction, clear differences emerged after correction. These showed up also in terms of statistical significance. When performing a repeated measures ANOVA for the beta2 and the beta1 band, the F-statistic for group differences doubled its value for the beta2 band due to correction of muscle artefacts, while it increased by 50% for the beta1 band.

3 log power beta 2 F7

power F7 (log uV^2)

12 10 8 6

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Fig. 3. Power spectrum at F7 (log-scale) for 20 s of EEG contaminated by muscle activity (dotted line) and without muscle activity (solid line).

Fig. 4. Box plots of beta2 band at F7 power for epochs with substantial muscle contamination and with negligible contamination before and after correcting for muscle artefacts (based on 20 epochs of 20 s for each group). Scale: log(mV2).

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log power muscle

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derivation Fig. 5. Average band power in the muscle band (51.0–69.0 Hz) across derivations for normal subjects (nZ66, solid line), presenile AD patients (nZ44; dotted), senile AD patients (nZ34, dashed). Scale: log(mV2).

4. Discussion

log power beta 2 (corrected)

log power beta 2 (uncorrected)

There is a general agreement that surface EMG activity at EEG derivations stretches well into the beta frequency range, creating a serious source of artefacts for beta1 and beta2 band power (Gotman et al., 1975; Nunez, 1981; O’Donnell et al., 1974; see also Fig. 2). It is, therefore, impossible to disentangle EEG and EMG activity by linear filters, and very difficult by non-linear filters. While we have a source signal for correcting EOG artefacts, there is no such 1.8 1.6 1.4 1.2 1.0 0.8 0.6 F7

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derivation Fig. 6. Average log beta2 band power for normal subjects (solid line), presenile AD patients (dotted) and senile AD patients (dashed). Above before muscle correction, below after. Scale: log(mV2).

signal for EMG artefacts. This is the reason for avoiding in our approach the correction of contaminated EEG traces and concentrating on the correction of EMG artefacts in fast band power (or similarly for other summary parameters reflecting fast activity). Blind source separation via independent component analysis might be an interesting approach, when muscle artefacts have to be corrected in the EEG trace itself, event related potentials being an example (Jung et al., 2000). The contribution of the surface EMG to band power is negligible for the alpha1 band, moderate for the alpha2 band but can become substantial for the beta1 band and even more so for the beta2 band (Table 1; a beta3 band above 25 Hz would be even more affected). This holds for the EEG at rest—when some task with facial expressions is involved, the effects might be bigger. As to be expected, the EMG affects most heavily frontal and temporal derivations and less central and parietal ones. When comparing the topographic profiles of muscle power and beta power, one sees that muscle power is minimal at the midline and increases steeply away from the midline. Vice versa, beta power is maximal at the midline. This is consistent with the contribution of EMG surface activity to fast band power in terms of squared correlation (Table 1). This topographic pattern underlines the need for correcting EMG artefacts in beta band power. We have chosen power in the band 51.0–69.0 Hz as a marker for the extent of muscle contamination, since the surface EMG has substantial power in this band and the EEG practically none. Power in high frequency bands has been used previously for the detection of EMG contamination (e.g. MacGillivray and Wadbrook, 1975). We could confirm that the size of muscle power agrees well with a visual scoring of the extent of muscle artefacts, proving it to be a useful EMG marker. The correction method is based on using power in the muscle band as a predictor for the extent of EMG contamination in a fast EEG band. This regression model allows it to eliminate to good approximation the power due to the surface EMG (note that the same model can be used in further studies when using similar recording conditions). The slope of this regression model is about the same for AD patients and controls, which is what we expect. There is no reason to postulate differences in the relative frequency content of the surface EMG. This correction method could be successfully validated (Fig. 4). While epochs contaminated by muscle artefacts showed a much higher median beta power than uncontaminated ones, their medians came very close after correction. Variability also decreases for contaminated data as expected since one source of variability has been eliminated. A controlled experiment was performed by adding portions of EEG contaminated by muscle artefacts in the same subject to a ‘clean’ epoch. This quasi-realistic evaluation show that the proposed correction method is able to eliminate most of muscle induced power in fast bands (Fig. 2).

T. Gasser et al. / Clinical Neurophysiology 116 (2005) 2044–2050

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Table 1 Squared correlation coefficients between muscle power and EEG band power for normal subjects (group 0, nZ66), presenile subjects (group 1, nZ44) and senile subjects (group 2, nZ34) F7 Group 0 .00 a1 .01 a2 b1 .20 b2 .33 Group 1 a1 .38 a2 .47 b1 .77 .78 b2 Group 2 a1 .09 a2 .35 .48 b1 b2 .52

F3

Fz

F4

F8

T3

C3

Cz

C4

T4

T5

P3

Pz

P4

T6

O1

O2

.01 .01 .05 .14

.00 .01 .01 .03

.00 .00 .03 .06

.01 .00 .07 .12

.01 .00 .10 .28

.01 .00 .02 .05

.00 .00 .02 .03

.01 .00 .00 .01

.00 .00 .10 .27

.03 .04 .01 .05

.02 .01 .01 .03

.00 .00 .00 .01

.00 .00 .00 .00

.00 .00 .05 .07

.02 .02 .00 .02

.00 .02 .00 .02

.16 .19 .41 .49

.14 .22 .27 .32

.13 .19 .34 .44

.20 .23 .45 .61

.24 .35 .68 .76

.14 .13 .30 .33

.10 .14 .14 .18

.10 .21 .27 .41

.08 .14 .38 .51

.20 .20 .54 .66

.20 .18 .28 .36

.09 .08 .18 .30

.09 .11 .21 .33

.06 .06 .22 .33

.17 .11 .36 .46

.22 .10 .39 .49

.03 .34 .41 .47

.05 .26 .32 .44

.01 .26 .28 .44

.01 .21 .45 .64

.18 .41 .65 .72

.04 .15 .17 .33

.08 .26 .12 .16

.03 .24 .20 .37

.01 .15 .42 .63

.06 .18 .33 .50

.12 .13 .10 .20

.29 .17 .14 .21

.13 .19 .15 .33

.12 .26 .37 .59

.03 .07 .14 .30

.06 .15 .21 .37

In principle, it would be possible to determine the relationship between muscle power and fast band power separately for each individual, by using a relatively large number of epoch with and without contamination by EMG artefacts. The disadvantages are that this is more time consuming and that the results cannot be automatically used in future studies, and further, that results will be more variable than with the proposed group approach (a similar discussion took place for EOG correction). The AD study is a good example to illustrate the scientific relevance of the proposed EMG correction method. The usual precautions have been taken during the recordings and in the selection of epochs for the EEG to obtain data of good quality. Thus, this is a realistic example for an EEG study, different from experimental recordings using cranio-facial muscles. The extent of EMG artefacts, as indexed by power in the muscle band, is much higher for AD patients than for controls (Fig. 5) and differences are statistically highly significant (a patient group is prone to have more frequently artefacts). From the literature, we expect lower beta power for AD patients than for normal subjects. The more intense EMG artefacts could then increase artificially beta power in patients, thereby masking possible group differences. After correction, the group differences in beta power became in fact visually much more transparent (Fig. 6). The F-statistic indicating the strength of group differences doubles its value in the beta2 band and increases by 50% in the beta1 band. Depending on sample size and group differences in the beta range, we thus risk to miss true differences due to the inflation of beta power by EMG artefacts in some group, usually the patient group. In an alternative case, where we expect higher beta power for the group with more intense EMG artefacts, group differences would be artificially enhanced. In any case, there is a risk of biased results in fast bands due to EMG contamination.

Altogether this provides sufficient evidence that correction of the influence of EMG artefacts on power in the fast bands can be a valuable tool for research. Its application is relatively straightforward. For the EEG at rest, we recommend using the method for the beta2 and the beta1 and for the alpha2 band at least for frontal-temporal derivations most susceptible to muscle artefacts.

Acknowledgements We thank the Central Institute of Mental Health (Mannheim) for the permission to use these data. The financial support of the DFG via project K2 (Sonderforschungsbereich 258) is also gratefully acknowledged. We also thank Dr Valentin Rousson for his support in the data analysis.

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