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Ocular artifacts in children’s EEG: selection is better than correction
Biological Psychology 48 (1998) 281 – 300
Ocular artifacts in children’s EEG: selection is better than correction Riek J.M. Somsen *, Bert van Beek Department of De6elopmental Psychology, Uni6ersity of Amsterdam, Roetersstraat 15, 1018 WB Amsterdam, The Netherlands Received 15 October 1997; received in revised form 26 March 1998; accepted 21 April 1998
Abstract The electroencephalogram (EEG) during middle childhood may be highly distorted by the occurrence of eye and head movement artifacts. Between 5 and 12 years children display a great number of such artifacts. In the present study we studied different methods to assess EEG artifacts in children. Three artifact treatments were compared with the uncorrected EEG: one widely used method which corrected the EEG for electrooculogram (EOG) – EEG transfer and two methods which selected artifact-free EEG segments. The most effective method should selectively reduce the spectral power in the lower frequency bands and at the frontal regions which are most susceptible to eye artifacts. The results demonstrated that the selection procedure, which combined two criteria for the selection of artifact-free EEG segments, was superior. The procedure that corrected the EEG for EOG – EEG transfer unselectively removed spectral power across the whole scalp and across all frequency bands. Furthermore, part of the maturational change in frontal Alpha power was filtered out by the correction procedure. It was concluded that for the background EEG in children, it is better to carefully select artifact-free EEG segments than to correct for EOG – EEG transfer. © 1998 Published by Elsevier Science B.V. All rights reserved. Keywords: EEG artifacts; EEG power spectrum; Children; Background EEG; Absolute power; Coherence
* Corresponding author. E-mail: op – [email protected] 0301-0511/98/$ - see front matter © 1998 Published by Elsevier Science B.V. All rights reserved. PII S0301-0511(98)00041-6
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1. Introduction Brain maturation in children between 0 and about 16 years is marked by specific changes in the electroencephalogram (EEG) (Thatcher, 1991). With age, the amplitude of slow Delta activity between 1.5 and 3.5 Hz and Theta activity between 3.5 and 7.5 Hz decreases and Alpha activity between 7.5 and 12.5 Hz increases (John et al., 1980; Ahn et al., 1980; Anokhin et al., 1996). It is very difficult to obtain reliable EEG records in young children, because their EEG’s usually have a high amount of artifacts. Young children display many eye blinks and other vertical eye movements (VEM), saccades and slow horizontal eye movements (HEM). In addition they often move their heads. Eye blinks cause a typical fast-rising peak in the electrooculogram (EOG) that lasts 70–120 ms. Head movements induce high amplitude EOG distortions which last much longer. These movements also induce distortions in the EEG records, in particular at the frontal channels. From the frontal towards the occipital brain sites the amplitudes of these distortions decrease. One method to remove artifacts, is the submission of EEG segments to an automatic regression procedure that corrects for EOG–EEG transfer (see Gratton et al., 1983; Woestenburg et al., 1983; Gasser et al., 1985; Brunia et al., 1989; Kenemans et al., 1991a,b). EEG correction studies on adult subjects have advocated different solutions for the computation of the regression coefficients. Some authors determine the regression coefficients in the time domain (Gratton et al., 1983), while others have argued that a frequency domain EOG–EEG correction method is superior (see Brunia et al., 1989). From a study in children, Gasser et al. (1985) concluded that not a fraction of the EOG activity is present in the EEG data, but a filtered version of the EOG, with a filter that attenuates the EOG at some frequencies more than at other frequencies. Hence, frequency domain correction was recommended. Frequency domain correction first transforms an EEG segment to the frequency domain and then estimates the vector of coefficients for the regression of EEG on EOG separately for each frequency step. Correction consists of the subtraction of the weighted EOG periodogram from the EEG periodogram (Kenemans et al., 1991b). Next the frequency data can be transformed back to the time domain. Automatic EOG – EEG transfer correction is commonly applied for ERP data (Brunia et al., 1989) and less often for the background EEG. For example, Van Beijsterveldt et al. (1996) and Van Baal et al. (1996) used automatic correction for EOG – EEG transfer on the EEGs of twins in studies on the genetics of the background EEG. Automatic correction implies that the same correction procedure is applied to all EEG records of all subjects and without evaluating the impact of this procedure on each individual EEG record and subject. However, as Gevins (1987) argued, there is no justifiable rationale for the reliance on automated algorithms. The second method to treat artifacts is the selection of EEG segments on the basis of visual inspection after instructions to avoid eye and head movements. This method was used in most early studies on EEG in children (Oken and Chiappa, 1988; Pivik et al., 1993; Anokhin et al., 1996). Visual selection may lead to much
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data loss in young children. Moreover, visually selected EEG may still be contaminated with low amplitude artifacts that cannot be distinguished from the genuine cerebral background EEG. John et al. (1983) reported that slow HEMs and muscle potentials are most commonly missed by artifact detection algorithms. Most EEG artifact studies have paid little attention to the specific characteristics of young children that may enhance the probability of artifacts in their EEGs. The first problem is that the amount of ocular artifact decreases with age. From age 6–7 years, the ability to control ocular movement highly improves. For younger children instructions to avoid eye movements often fail. Hence, until age 6 to 7 many times a very high proportion of the EEG records may be contaminated with eye movements. Moreover, young children usually display many combined eyehead movements (‘looking around’). Head movements induce severe distortions in the EEG which cannot be corrected by regression procedures. Hence EEG segments containing such distortions must be removed from the data-set. This causes a difference in the amount of EEG records that are free of artifacts in children of different ages. The second problem is that ocular and head movement artifacts and maturational changes both occur in the same frequency range from 0–10 Hz (Gasser et al., 1985). Hence, correction for ocular eye artifacts in the background EEG in children may have an unwanted effect on the assessment of the amount of maturational change. Evidently, the reduction in ocular artifacts and the decrease of low frequency EEG power may both be a function of increasing maturation. It is, however, unknown how these two variables are related. The third problem, in children is that the physical distance between the EOG and EEG electrodes is very small. In a study on children of around 10 years, Gasser et al. (1985) concluded that both the EOG and frontal EEG electrodes recorded eye movements as well as cerebral activity. Furthermore, they reported an unexpected high correlation between EEG and EOG power in the Alpha band. The presence of EEG activity in the EOG may lead to confounding effects when correcting for EOG – EEG transfer. The frontal EEG activity in the EOG derivations may ‘correct’ part of the activity in the EEG derivations. Since it is likely that the correlation between EOG and frontal EEG in young children is relatively high, this transfer problem may be much greater in children than in adults. Finally, eye blinks (VEM) may be easy to detect due to their characteristic shape and amplitude, but lower amplitude slow frequency artifacts caused by HEM and genuine cerebral slow activity are difficult to distinguish. This contamination of genuine and artifactual spectral powers is even more problematic in children, because children not only exhibit more artifacts, but also have a relatively higher power in the lowest frequency bands. Since both the EOG and EEG electrodes record the same artifactual and genuine cerebral activity, both the traditional selection by visual inspection and the more recent EOG–EEG transfer correction procedures fail with respect to this problem. From the above list of additional errors that may contaminate the analysis of EEG data in children, it appears that these EEGs require an even more careful procedure to detect, remove and/or correct ocular artifacts. Therefore, we con-
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structed a semi-automatic selection procedure which first detects blinks and other high amplitude distortions in the raw EEG and next defines EEG epochs in segments which do not contain artifactual activity. In addition, we developed a protocol for the selection of EEG epochs that contain only true cerebral activity and no slow and low amplitude artifacts due to saccadic and slow HEM. Our main assumption was that the artifactual activity and the true EEG activity may have different distributions. Most likely, the low frequency cerebral activity will be evenly distributed across the whole EOG time series. Hence, it occurs in all segments of the EEG. In contrast, the low frequency artificial activity will have a periodical on-off character. During an ‘on’ period the artificial activity is added to the cerebral low frequency activity, during an ‘off’ period it is not added to the cerebral activity. When we rank-order the EOG segments on the basis of the amount of power in the lower frequency bands, e.g. 1–6 Hz, the summed cerebral and artificial power will occur in the highest ranks, while the lowest ranks will exhibit only cerebral power. Hence, removal of a percentage of epochs with the highest ranks, will reduce the artificial low amplitude activity, while the true cerebral low frequency activity will remain in the data-set. The above reasoning, resulted in a selection method based on the ranking of epochs on the basis of the amount of low frequency EOG power. This method is similar to an early method used by Mocks and Gasser (1984). It was the purpose of the present study to determine the best artifact treatment procedure for children between 5 and 12 years who took an Eyes Open and Eyes Closed background EEG task. The Eyes Open and Closed tasks are associated with different types of EOG artifacts (Gasser et al., 1985). During Eyes Open VEMs (and head movement due to looking around) are most prominent. During Eyes Closed no blinks, but mainly HEMs occur. The conditions were: (1) all epochs are included; no correction or selection is carried out (RawSignal); (2) automatic EOG – EEG transfer correction of all epochs (correction); (3) detection and removal of epochs with high amplitude artifacts such as blinks and head movements (HighAmp); and (4) the same as (3) with further selection of epochs using a 35% rank-order rejection criterion to remove the EOG epochs with the highest power in the 1 – 6 Hz band (HighLowAmp). To directly compare the contribution of two different selection methods, a fifth condition was added which only removed 50% of the epochs that had the highest EOG power in the 1–6 Hz band (LowAmp). The most effective method of artifact treatment should (Hypothesis 1) reduce low frequency spectral power (Delta and Theta) in the anterior channels (Frontal and to a lesser extend Central) and should (Hypothesis 2) not reduce high frequency power (Alpha and Beta) in anterior, central, or posterior channels. Since, the EEG data of the children show a highly significant decrease with age in the lower frequency bands Delta, Theta and increase with age in the amount of Alpha power, (Somsen et al., 1997), it was hypothesized that Age effects would interact with the Artifact conditions. We expected that the youngest children would profit most from the combined HighLowAmp condition (Hypothesis 3).
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2. Method
2.1. Subjects A total of 142 healthy children (72 girls and 70 boys) from two suburban primary schools served as subjects. There were eight age groups ranging from 5 to 12 years. Boys and girls were approximately equally distributed across the age groups: respectively, (11, 7), (6, 9), (9, 6), (9, 13), (8, 11), (9, 10), (10, 6) and (8, 10) children.
2.2. Physiological recordings The EEG was recorded from 15 channels: F7, Fz, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1 and O2 according to the international 10–20 system, with an electro-cap referred to the right mastoid. Electrode impedance was kept below 8 KOhm. Since there were only 17 polygraph channels available, we used two electrodes for the EOG. These were mounted such that the vertical and HEM were recorded at both electrode locations which was referred to mastoid (Wijker, 1991). One electrode was placed lateral inferior to the canthus of the right eye and the other lateral superior to the canthus of the left eye. Both electrodes recorded vertical and HEM, whereby the same eye movements were reflected in opposite going polarities. These 17 recording channels were amplified and printed on an analogue record using a Nihon-Kohden polygraph. Beckman Ag–Ag/Cl electrodes were used for reference and EOG recording. Data acquisition was under control of a Comtrad-AT interfaced with a Keithley system. EEG and EOG were digitized to A–D values (between 0 and 4096) at 100 Hz during 2 min. Calibration values were recorded once before and once after the experimental tasks. The EOG and EEG data were transformed off-line to mV using the averaged calibration values.
2.3. Recording conditions The children took a neurometric test battery (see Somsen et al. (1990) for a description of this test battery) that consisted of six attentional tasks presented in a fixed order. The first two tasks are reported here. These tasks consisted of 2-min periods of quiet rest with eyes open and eyes closed. The experimental equipment and the child were situated in adjacent rooms. There were two experimenters, one controlled the equipment, visually checked the EEG recordings and initiated the experimental sub-tasks. The second experimenter was sitting behind the child instructing the child to avoid unnecessary body, head and eye movements. Before a task, the experimenter informed the child about the task requirements. The child was sitting in a comfortable reclining chair in front of a Macintosh-Plus computer screen. During the task the child was looking at a dot on the computer display. To reduce test anxiety, children were informed about the experimental procedures by a video film a couple of days before the recording session.
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2.4. Data pre-processing The EEG data were processed by a specially developed program. This program incorporates an earlier brain mapping program (BM) which has been described by Somsen et al. (1994). The data were automatically screened for ‘flat spots’, defined as five or more consecutive points having the same value and for EEG records that had shifted off-scale; i.e. beyond the upper or lower boundaries of the Keithley A/D window. Next the data were calibrated and transformed from A/D units to mVolts. Effects of muscle activity on the EEG from the jaws and neck were screened by a standard deviation criterion in a moving window (4 standard deviations in window of 3 samples). If one of the above described errors was detected in a channel epoch, the epoch for this channel was discarded from the data analysis. Epochs of 2.56 sec that overlapped by 20% were defined in the 2 min EOG/EEG records in 17 channels. First, EOG segments that contained high amplitude artifacts were marked. Next, the EOG/EEG epochs were defined in the unmarked EOG segments. The complete data set was processed four times with different artifact treatments (RawSignal, correction, HighAmp, HighLowAmp). For the RawSignal condition no selection or correction was applied. The HighAmp selection condition consisted of the definition of epochs in EOG segments which did not contain high amplitude distortions. These distortions from eye blinks and head movements were detected using a template matching routine. The amplitude (mV) and duration (ms) of the rising flank of the template could be adjusted to each individual child. Epochs were determined in those parts of the EOG time series that were free of blinks and other high amplitude errors. For the HighLowAmp condition the epochs which remained after HighAmp selection were rank-ordered according to the amount of EOG power in the 1–6 Hz band. The 35% epochs with the highest ranks were removed from the data. It appeared that for some of the younger children too few (less than eight out of the original set of 57) epochs remained in the HighLowAmp condition. For these children the 35% rejection criterion was individually adjusted to a lower percentage, while incidentally artifact-free epochs were defined and added on the basis of visual inspection. To evaluate the impact of the different selection criteria, in one ANOVA HighAmp and HighLowAmp were compared with a fifth selection condition (LowAmp) in which no blink detection was carried out, but in which 50% of all epochs were selected on the basis of the lowest rank of the averaged power in the 0–6 Hz band of the EOG. For the correction condition the ‘matched filter’ procedure described by Brillinger (1975) for EOG – EEG transfer correction in the frequency domain was applied. After Fast Fourier Transformation of the EEG epoch, this procedure determines a gain function which indicates by how much a signal is attenuated or amplified at each frequency and a phase function which gives the delay between the two signals. This results in a vector of complex valued coefficients for each frequency step. Correction consisted of the subtraction of the weighted EOG periodogram from the EEG periodogram.
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A problem, that has attracted little attention in the ERP correction literature is the setting of the frequency range for which the correction for EOG–EEG transfer is carried out. The maximum frequency (Nyquist frequency) is usually set to the maximum frequency in the EEG record (e.g. Gasser et al., 1985; Kenemans et al., 1991b). However, the EOG – EEG transfer artifacts mainly occur at the lowest frequencies. Gasser et al. (1986) concluded that HEM–EEG transfer occurs between 0 – 6 Hz (anterior) and 0 – 4 Hz (posterior), while VEM–EEG transfer occurs between 0 – 8 Hz (anterior) and 0–11 Hz (posterior). Hence, correction beyond these frequencies seems unnecessary and may even lead to the false removal of true cerebral EEG.
2.5. Data analyses After pre-whitening (10% Cosine-Bell window) power spectral analysis on the EOG and EEG epochs using a Fast Fourier Transform routine was carried out. The EEG and EOG were decomposed into the amount of energy (micro Volt squared mV2) at frequency steps of 0.3956 Hz. The lower part of this spectrum (1.5 – 20 Hz) was analyzed. The power spectra were averaged across artifact-free epochs. Smoothing of the spectra was achieved by this averaging (Sturgis, 1983). Broad band frequencies were summed to obtain six indices (absolute power density): Delta (1.5 – 3.5 Hz), Theta (3.5–7.5 Hz), Alpha1 (7.5–10.0 Hz), Alpha2 (10.0 – 12.5 Hz), Beta1 (12.5 – 15.0 Hz) and Beta2 (15.0–19.5 Hz). To achieve normal distributions, all analyzed EEG data were Log10 transformed (Gasser et al., 1985). All figures are based on Log10 absolute power values.
2.6. Statistical tests Repeated measures ANOVA’s (BMDP4V) were carried out with Age Group (8), Artifact Treatment (4) and Region (4) as factors for the Eyes Open and Eyes Closed conditions and for each broad band index. For the Region factor the spectral indices were averaged across the Frontal (F7, Fz, F8), Central and Central/Temporal (T3, C3, Cz, C4, T4), the Parietal and Parietal/Temporal (T5, P3, Pz, P4, T6) and the Occipital electrodes (O1, O2). Hence, the four regions represented increasing distances from the eyes. For the between subject and repeated measures tests the Huynh–Feldt corrected F statistic was used (pB 0.05). Since for each level of the Artifact factor, the same data-set was used (after passing one of the artifact treatments), we preferred to analyze the data as difference scores. Therefore most post-hoc ANOVA’s were carried out on the EEG spectra expressed as difference from the RawSignal condition. Specific post-hoc ANOVA’s were carried out to study small differences between the selection conditions. Different age effects were also explored by comparing the 5, 6, 7 year olds with the 8, 9, 10, 11, 12 year olds and the youngest 5-year-old children with the oldest 12-year-old children. Intra-hemispheric coherency spectra were also analyzed. The selected coherency spectra represented three increasing distances from the frontal electrodes on the
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scalp: (1) averaged coherence between F7 and T3, F7 and C3, F8 and C4, F8 and T4; (2) averaged coherence between F7 and T5, F7 and P3, F8 and P4, F8 and T6; (3) averaged coherence between F7 and O1, F8 and O2. The ANOVA factors for the coherence data were: Age (8), Artifact Condition (4) and Region (3).
3. Results
3.1. Number of artifacts detected Do younger children exhibit more artifacts than older children? The number of epochs that remained after artifact detection by template matching of the EOG (eye-blinks detected in the HighAmp condition) was used as index for the amount of artifacts in the EEG of each child. Simple F-tests were carried out for Eyes Open and Eyes Closed to assess whether the averaged number of selected epochs varied with Age. For Eyes Open the number of selected epochs significantly increased with Age from 19 to 38 epochs (F(7, 129)= 8.48). For Eyes Closed this increase was from 18 to 36 epochs (F(7, 123) = 5.78). Hence, the EEGs of the younger children contained much more eye artifacts than the EEGs of the older children. These results further indicated that the oldest (12-year-old) children still exhibited a substantial amount of artifacts, since only 63% of the epochs (36 out of 57 possible epochs with 20% overlap) were accepted.
3.2. EEG power spectra Are the EEG power spectra differentially influenced by the four artifact treatments? Table 1 provides a summary of ANOVA’s that were conducted to answer this question for the Eyes Closed and Eyes Open task, for each broad band absolute EEG power index. In this set of ANOVA’s the four artifact conditions were included (Table 1). A second set of ANOVA’s on the EEG difference scores (the correction, HighAmp and HighLowAmp data subtracted from the RawSignal data) yielded highly similar effects. These results are not reported separately. The ANOVA’s for each broad band showed highly significant main effects of Artifact and Region. The Artifact main effects confirmed that the different artifact treatments differentially affected the data set. The Region main effects confirmed the well-known decrease of the influence of eye artifacts from frontal to occipital regions. Table 1 summarizes the interactions which include the Artifact factor. The main interaction of interest for this study is the Artifact× Region interaction. This interaction was highly significant for all broad band indices. Furthermore there were significant higher order interactions with Age. These interactions are further analyzed and discussed below. Fig. 1 depicts the Eyes Open, Artifact× Region interactions. It shows the four EEG power band amplitudes averaged across all age groups for each Artifact condition and scalp Region (indicated by the capitals F (frontal), C (Central), P (parietal) and O (occipital) on the X-axis). Inspection of the figure indicates that in
Alpha2
Beta1
* pB0.05, ** pB0.01, Huynh – Feldt adjusted df. All main effects of Artifact and Region are pB0.01.
674.67** (2,239) 644.84** (2,281) 628.01** (3,317) 629.78** (2,287) 1.63* (21,342) 1.94* (9,169) 3.94* (9,157) 1.51 (15,272) 1.67 (13,239) 1.68* (16,280) 2.58** (18,317) 2.11* (16,287)
Alpha1
Eyes Closed: level Artifact×Region (4, 4) 342.87** (3,311) Age×Artifact (8, 4) 1.10 (15,268) Age×Artifact×Region (8, 4, 4) 1.25 (18,311)
Theta
558.21** (3,324) 449.07** (3,317) 478.37** (3,346) 372.53** (2,686) B1 2.16* (13,238) 1.76* (12,227) 1.75* (14,271) 1.34 (17,324) 1.18 (17,317) 2.21** (18,346) 2.75** (15,286)
Delta
Artifact×Region (4, 4) 468.21** (3,351) Age×Artifact (8, 4) 2.18* (13,238) Age×Artifact×Region (8, 4, 4) 1.15 (19,351)
Eyes Open: level
Table 1 Summary of ANOVA’s on EEG power
649.38** (3,318) 1.34 (14,258) 1.87* (18,318)
196.80** (2,242) 1.53 (13,248) 1.97* (13,241)
Beta2
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the RawSignal curves the Frontal Delta and Frontal Theta power amplitudes were increased relative to the more distant C, P and O regions. In the Alpha and Beta bands this difference was absent. Hence, these RawSignal plots indicate that there was additional power at specific data points. The scalp regions and frequency bands of these data points corresponded exactly to the scalp regions and frequencies that were expected to be most sensitive to eye movement artifacts, i.e. the anterior lower frequencies. The figure further indicates that the data points that were relatively increased in the RawSignal curves were not increased in the selection conditions. It is important to note that the RawSignal and the selection conditions concern untransformed EEG data. The difference between the RawSignal and selection signals was that on the basis of certain criteria, only part of the data was included in the selection conditions. Hence, inspection of the curves of these two conditions indicated (a) that the relatively increased Frontal Delta and Frontal Theta data points may have been influenced by artifacts and that (b) that these specific data points changed when only part of the data was selected. We conclude that the results of the two selection conditions both satisfied the predictions of Hypothesis 1 and of Hypothesis 2 that artifact removal should affect only the anterior low frequency bands and that the other frequencies and bands should not be affected.
Fig. 1. Averaged power during Eyes Open in the six broad band parameters (Delta, Theta, Alpha1, Alpha2, Beta1, Beta2) at four regions: F= Frontal, C= Central, P= Parietal and O=Occipital. Each line depicts a data treatment procedure: All data included (RawSignal), EOG – EEG transfer correction (correction), blink detection (HighAmp) and blink detection followed by selection of epochs which have relatively low power in the 1–4 Hz band of the EOG spectrum (HighLowAmp).
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Fig. 1 further illustrates that correction produced a substantial reduction in power amplitude across all broad bands and across all regions. Power reduction was relatively greater at the frontal sites than at the more distant sites. Power reduction occurred both for the slow (Delta, Theta), but also for fast (Alpha, Beta) frequency bands. This latter result was obviously an effect of the conventional setting of the Nyquist frequency to the maximum frequency in the spectrum. This caused an overall reduction of power across the whole frequency range. However, the amount of power reduction was surprisingly high. Hence, it is likely that in addition to the artifactual power, also true EEG power recorded at the EOG electrodes was removed from the EEG in the correction condition. We conclude that the effects of correction were not in accordance with Hypothesis 1 and 2. Do the youngest children profit more from specific artifact treatments than the oldest children? To answer this question, the amount of EEG power reduction relative to the RawSignal was compared between the 5 and 12-year-old children. During Eyes Open the amount of Delta power reduction (Fig. 2, upper panel) in the 5-year-olds appears relatively increased across all four regions. This effect seemed relatively smaller during Eyes Closed (Fig. 3). Since most artifacts were expected at Frontal Delta, a post-hoc ANOVA on the 5 and 12-year-old groups (2) with correction versus HighLowAmp (2) and Region (4) as factors was carried out. This analysis yielded no significant effect for Eyes Closed and a significant Age ×Artifact interaction for Eyes Open: (F(1, 31)= 4.99). Correction reduced the same amount of Delta power in the two groups (5-year-old: 0.28 mV2, 12 year olds: 0.30 mV2). In contrast, the HighLowAmp condition removed more power in the 5-year-old than in the 12-year-old children (respectively, 0.23 and 0.17 mV2). We conclude that when a relatively high amount of artifacts were present (Eyes Open), the youngest children profited more from careful epoch selection than the oldest children. This finding confirmed Hypothesis 3. Fig. 2 indicates that the 5-year-old children showed an small increase in Alpha power after selection at the Central, Parietal and Occipital regions (Fig. 2, upper panel). This increase in Alpha was greater in the 5-year-olds than in the 12-yearolds (F(1, 31) = 6.62). Gasser et al. (1985) also reported that during Eyes Open, Alpha power was higher at epochs without EOG artifacts than at epochs with EOG artifacts. We assume that increased muscle activity during epochs with artifacts in these children may have caused Alpha desynchronization. Hence, when the epochs with increased muscle activity were removed, relatively more Alpha power may have remained in the selected epochs. Hence, this increase in Alpha indicates that more eye muscle artifacts were present in the 5-year-old children. Inspection of Fig. 3 (lower panel) indicates that the correction condition induced increased power reduction in the Alpha1, Alpha2 and Beta1 bands in the oldest children. The increase in power reduction occurred in particular during Eyes Closed (Fig. 3, lower panel). A post-hoc ANOVA with Age 5/Age 12 (2) × Region (4) as factors on the Eyes Closed correction condition showed no Age effects for Delta and Theta, significant main effects of Age in Alpha1 and Alpha2 and significant Age ×Region interactions in the Alpha1 (F(2, 49)=4.54), Alpha2 (F(2, 50)= 7.83),
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Fig. 2. Averaged power during Eyes Open in the 5-year-old children (upper panel) and in the 12-year-old children (lower panel). The amount of power reduction relative to the RawSignal condition is depicted for the correction, HighAmp and HighLowAmp conditions and for the Delta, Theta, Alpha1, Alpha2 and Beta1 broad band parameters at the frontal, central, parietal and occipital regions.
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Fig. 3. Averaged power during Eyes Closed in the 5-year-old children (upper panel) and in the 12-year-old children (lower panel). The amount of power reduction relative to the RawSignal condition is depicted for the correction, HighAmp and HighLowAmp conditions and for the Delta, Theta, Alpha1, Alpha2 and Beta1 broad band parameters at the frontal, central, parietal and occipital regions.
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Beta1 (F(1, 43) = 9.35), Beta2 (F(1, 36)= 4.91) bands. In these higher frequency broad band indices, relatively more power reduction occurred in the 12-year-old children. Power reduction was relatively increased at the Frontal and Occipital sites of the Alpha1 band (see Fig. 3). A study of Somsen et al. (1997) on this same dataset reported that Occipital Alpha increased with age and that Frontal Alpha power increased between 10 and 12 year olds in particular during Eyes Closed. Hence, this result indicates that part of this maturational change was filtered out by the correction condition. We assume that Eyes Open is more prone to artifacts than Eyes Closed. Hence the selection conditions should differentiate between these two tasks. A post-hoc ANOVA was carried out on the Delta band (difference from RawSignal) for the Eyes Open and Eyes Closed selection conditions. An ANOVA was carried out with all Age groups (8), the Eyes Open and Closed Tasks (2), the Frontal and Central Regions (2) and the HighAmp and HighLowAmp selection conditions (2) as factors. The results showed highly significant main effects of Task (F(1, 116)= 145.10), Region (F(1, 116) =514.34) and selection condition (F(1, 116)=136.97). The HighLowAmp condition induced more power reduction than the HighAmp condition (respectively, 0.20 and 0.17 mV2). The highest reduction occurred in the HighLowAmp condition at the Frontal region. A significant interaction of Task× Region (F(1, 116) = 187.39) confirmed that Frontal Delta reduction was much higher for Eyes Open (0.37 mV2) than for Eyes Closed (0.15 mV2) while Central Delta reduction was less different between the two tasks (respectively, 0.14 and 0.7 mV2) (see Fig. 4). Age group showed a marginally significant main effect (F(7, 116) = 1.97, p B0.07). Fig. 4 suggests that the amount of Delta power reduction initially increased from the 5 to the 7-year-olds and then decreased between 7 and 8 years, but did not decrease further with increasing age. To further analyze the difference between 5–7 and 8–12 years in relation to the different selection conditions, the subjects were split into a younger (age 5, 6, 7 year) and older Age group (8, 9, 10, 11, 12 year). The three selection conditions: HighAmp, HighLowAmp and LowAmp were compared. Power values were expressed as difference from RawSignal. Only the Frontal and Occipital regions were compared. Hence, the factors in the ANOVA’s were: Age (2)× Artifact (3)× Region (2). The results for the Delta band showed highly significant main effects of Age (Eyes Open: F(1, 134) =11.61; Eyes Closed: F(1, 134)=11.61) and significant interactions of Age× Artifact ×Region (Eyes Open: F(2, 380)= 8.82; Eyes Closed: F(3, 408) = 2.83). These effects were also significant for the Theta band, but not for the Alpha and Beta bands. Fig. 5 illustrates the Eyes Open results. The reduction in power induced by the selection conditions was highest in the Delta band and at the Frontal region. The combined selection condition (HighLowAmp) showed the greatest power reduction for Delta in the youngest children. The LowAmp condition induced power reduction that was similar, but somewhat less than the two other selection conditions. Recall that LowAmp was based on the removal of 50% of epochs with greatest amount of low frequency EOG activity. This 50% criterion
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Fig. 4. Comparison of the amount of power reduction (from the RawSignal condition) in the Delta band between the HighAmp and HighLowAmp selection conditions and between the Eyes Open and Eyes Closed tasks at the Frontal and Central regions for eight Age groups.
was arbritarily chosen. The interactions supported our hypothesis that the combined VEM (HighAmp, blink) and HEM (LowAmp, low frequency) selection methods would be most effective. The combination of High and LowAmp selection specifically removed most power in the frontal Delta band of the youngest children.
3.3. Coherence results Do the four artifact treatments differentially affect the EEG Coherence spectra? The ANOVA’s with Age (8), Artifact Treatment (4) and Region (3) as factors on the coherence data showed highly significant main effects of Artifact and Region and significant interactions of Age group× Region and of Age × Artifact and of Artifact ×Region. During Eyes Closed the Alpha1 (F(19, 342)= 1.99 pB 0.008) and Alpha2 (F(20, 353) =1.91 pB0.01) indices showed significant higher order interactions with Age. The main result of the coherence analyses was an Artifact× Region interaction which is illustrated in Fig. 6 for Alpha1 Eyes Open (F(3, 367)= 187.90) and Eyes Closed (F(3, 342)= 150.63). The Figure shows that coherence was systematically lower in the correction condition as compared with the other three conditions. Fig. 6 also illustrates the interaction with Region when the distance between electrode pairs increased, the additional coherence reduction decreased. We conclude that correction systematically depressed intra-hemispheric coherence.
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4. Discussion Four different artifact treatments for EEG power spectra in young children between 5 and 12 years with a high percentage of artifacts were compared. In young children reliable EEG measurement is problematic, because children of this age have difficulty to control their eye and head movements after instructions to do so. This was confirmed by the high amount of detected EOG artifacts. The amount of artifacts significantly decreased with age. The plotted RawSignal curves indicated the data points that were most susceptible to artifactual influences on the background EEG, power values were clearly elevated in the Frontal Delta and Frontal Theta measures. This result was consistent with our Hypothesis 1 which predicted that the low frequency Frontal and Central regions would be most influenced by eye movement artifacts. The RawSignal Alpha and Beta measures did not exhibit such elevations. Moreover, the curves of the selection conditions did not show elevations in Frontal Delta and Theta. This difference supported our reasoning that a selected part of the EEG epochs may display the true cerebral EEG variance equally well as the complete set of epochs. However, the complete set displayed additional artifactual variance.
Fig. 5. Comparison of the amount of power reduction (from the RawSignal condition) in the youngest (first triple of bars) and oldest (second triple of bars) children between three selection procedures: HighAmp, HighLowAmp and LowAmp during Eyes Open. From left to right on the X-axis the clusters of six bars represent the amount of power reduction in Frontal Delta, Occipital Delta, Frontal Theta, Occipital Theta, Frontal Alpha1 and Occipital Alpha1.
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Fig. 6. Averaged coherence spectra in the RawSignal, correction, HighAmp and HighLowAmp conditions in the Alpha1 band during Eyes Open (left) and Eyes Closed (right). The coherences between the Frontal-Central, Frontal-Parietal and Frontal-Occipital regions are depicted on the X-axis for each artifact treatment condition.
Comparison of the different artifact treatments strongly indicated that selection of EOG artifact-free epochs is better than correction for EOG–EEG transfer. Relative to the RawSignal, the selection conditions induced only minor changes in power at the Alpha and Beta bands. This absence of change was predicted by Hypothesis 2. In contrast, correction induced reductions in power in all broad bands and at all regions. Power reduction at the Frontal regions was much higher than at the other regions. Also Alpha and Beta power were substantially reduced. This result is consistent with results of Gasser et al. (1986) who reported that correction reduced the higher frequencies. Obviously, this global effect of correction could have been partly prevented by using a smaller frequency window (Nyquist frequency). However, restriction to the Delta and Theta window would not solve the problem of over-correction. Fig. 1 illustrates that over-correction occurred at the Frontal sites of all broad band indices. This result raises the question why Frontal power reduction was so much higher for correction as compared with selection. We suggest that this may have been caused by the frontal EEG activity that was recorded by the EOG electrodes. It seems that the correction procedure not only removed shared artificial activity between the EOG and the EEG sites on the scalp, but also shared normal Frontal EEG activity. Also the increased removal of Frontal and to a lesser extend
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Occipital Alpha in the 12-year-old children suggests that normal maturational increase in power was partly removed by correction. This suggestion is supported by the coherence results. In the correction condition, the coherence between the F–C, F – P and F – O channels was much smaller than in the other conditions. Moreover, the region which showed the highest coherence (F–C) reduction also showed the greatest over-reduction of power. Since coherence after HighAmp and HighLowAmp did not differ much from the RawSignal coherence, we conclude that selection did not change the normal EEG coherence activity. Selection of artifact-free EEG epochs reduced more power in the Eyes Open task than in the Eyes Closed task. Moreover, power reduction during Eyes Open of Frontal Delta activity was much greater than of Central Delta activity. These findings are consistent with the assumption that an Eyes Open task is more prone to artifacts than an Eyes Closed task. During Eyes Closed, there is no visual input which largely prevents the occurrence of blinks and combined eye–head movements. During Eyes Open, HighLowAmp selection induced additional Frontal Delta reduction as compared with HighAmp. Also, the expectation that the youngest children who produced most artifacts, would benefit most from the double selection procedure was confirmed. In Fig. 5 the 5–7-year-olds showed relatively increased power reduction due to HighLowAmp compared with the 8–12-year-old children. This difference did not occur for the Theta band or for any of the other high frequency bands. Hence, this result indicated that the effect of the combined strategy was highly specific. The additional power reduction was restricted to, Eyes Open, 5 – 7-year-olds, Frontal Delta. Slow HEMs due to looking around, seems the most probable source for this additional power. Finally, it appeared that the HighAmp and LowAmp procedures affected common sources of artifacts. HighAmp was based on the concrete detection of eye blinks, whereas LowAmp was based on a general criterion to remove EOG epochs with the highest low frequency power. The setting of this criterion was arbitrary and did not vary between subjects. Apparently, blinks and head movements add low frequency power to the EOG and EEG spectra. Power variance due to artifacts is added to the variance of the cortical sources. These two sources of variance may be distinguished by the different nature of their distributions across the EEG record. We conclude that the avoiding of EEG segments with eye blinks and the subsequent removal of epochs on the basis of the more robust LowAmp procedure is an adequate strategy to prevent that the EEG is contaminated with eye and head movement artifacts. The present results indicated that the automatic correction of EEG artifacts should be avoided, because this may have undesirable effects on the EEG data. For example, in the twin studies of Van Baal et al. (1996) and Van Beijsterveldt et al. (1996) correction for EOG – EEG transfer of the background EEG was applied. In the youngest twins automatic correction followed the removal of epochs with artifacts on the basis of visual inspection (Van Baal et al., 1996). Their results showed that the correlations and heritabilities were relatively lower in the Frontal Delta band than in the Theta, Alpha and Beta bands. The authors attributed this
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finding to the relatively increased contribution of eye movements in the Delta band. The present data showed that correction produces over-correction of Frontal Delta. Hence, we suspect that not the eye movements itself, but the correction method that was chosen to remove the eye movements may have negatively influenced the results in these studies. With respect to ERP data, it is, as yet, uncertain whether correction has any negative influence on the ERPs of young children. We assume that this may depend on the extend to which task relevant cerebral activity is removed after correction. From the current results, we suggest, that both EEG and ERP data measured in young children require a very careful artifact removal procedure.
References Ahn, H., Pricep, L., John, E.R., Baird, H., Trepetin, M., Kaye, H., 1980. Developmental equations reflect brain dysfunction. Science 210, 1259 – 1262. Anokhin, A.P., Birbaumer, N., Lutzenberger, W., Nikolaev, A., Vogel, F., 1996. Age increases brain complexity. Electroencephalogr. Clin. Neurophysiol. 99, 63 – 68. Brillinger, D.R., 1975. Time Series: Data Analysis and Theory. Holt, Rinehart and Winston, New York. Brunia, C.H.M., Mocks, J., van den Berg-Lenssen, M.M.C., 1989. Correcting ocular artifacts in the EEG: A comparison of several methods. J. Psychophysiol. 3, 1 – 50. Gasser, T., Sroka, L., Mocks, J., 1985. The transfer of EOG activity into the EEG for eyes open and closed. Electroencephalogr. Clin. Neurophysiol. 61, 181 – 193. Gasser, T., Sroka, L., Mocks, J., 1986. The correction of EOG artifacts by frequency dependent and frequency independent methods. Psychophysiology 23, 704 – 712. Gevins, A.S., 1987. Overview of computer analysis. In: Gevins, A.S., Remond, A. (Eds.), Methods and Analysis of Brain Electrical and Magnetic Signals. EEG Handbook, vol. 1. Elsevier, Amsterdam, pp. 31–81. Gratton, G., Coles, M.G.H., Donchin., E., 1983. A new method for off-line removal of ocular artifact. Electroencephalogr. Clin. Neurophysiol. 55, 468 – 484. John, E.R., Ahn, H., Pricep, L., Trepetin, M., Brown, D., Kaye, H., 1980. Developmental equations for the electroencephalogram. Science 210, 1255 – 1258. John, E.R., Prichep, L., Ahn, H., Easton, P., Fridman, J., Kaye, H., 1983. Neurometric evaluation of cognitive dysfunctions and neurological disorders in children. Prog. Neurobiol. 21, 239 – 290. Kenemans, J.L., Molenaar, P.C.M., Verbaten, M.N., 1991b. Models of estimation and removal of artifacts in biological signals. In: Weikunat, R. (Ed.), Digital Biosignal Processing. Elsevier, Amsterdam, pp. 231–250. Kenemans, J.L., Molenaar, P.C.M., Verbaten, M.N., Slangen, J.L., 1991a. Removal of ocular artifact from the EEG: A comparison of time and frequency domain methods with simulated and real data. Psychophysiology 28, 114–121. Mocks, J., Gasser, T., 1984. How to select epochs of the EEG at rest for quantitative analysis? Electroencephalogr. Clin. Neurophysiol. 58, 89 – 92. Oken, B.S., Chiappa, K.H., 1988. Short-term variability in EEG frequency analysis. Electroencephalogr. Clin. Neurophysiol. 69, 191–198. Pivik, R.T., Broughton, R.J., Coppola, R., Davidson, R.J., Fox, N., Nuwer, M.R., 1993. Guidelines for the recording and quantitative analysis of electroencephalographic activity in research contexts. Psychophysiology 30, 547–558. Somsen, R.J.M., Molenaar, P.C.M., Van’t Klooster, B.J., 1990. A computer testbattery for the assessment of elementary learning, attentive and effort processes in LD and ADD children. In: Koops, W., Soppe, H.J.G., van der Linden, J.L., Molenaar, P.C.M., Schroots, J.J.F. (Eds.), Developmental psychology behind the dykes. Developmental psychological research in the Netherlands. Eburon, Delft, pp. 169–180.
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Somsen, R.J.M., van Beek, B., Huizenga, H.M., Molenaar, P.C.M., 1994. BM: A multifunctional EEG Brain mapping program for the PC. In: Maarse, F.J., Akkerman, A.E., Brand, A.N., Mulder, L.J.M., van der Stelt, M.J. (Eds.), Computers in Psychology 5. Applications, Methods and Instrumentation. Swets and Zeitlinger, Lisse, pp. 194 – 205. Somsen, R.J.M., van’t Klooster, B.J., Van der Molen, M.W., Van Leeuwen, H.M.P., Licht, R., 1997. Growth spurts in brain maturation during middle childhood as indexed by EEG power spectra. Biol. Psychol. 44, 187–209. Sturgis, S.P., 1983. A spectral-analysis tutorial with examples in FORTRAN. Behav. Res. Method Instrum. 15, 377–386. Thatcher, R.W., 1991. Maturation of the human frontal lobes: physiological evidence for staging. Dev. Neuropsychol. 7, 397–419. Van Baal, G.C.M., de Geus, E.J.C., Boomsma, D.I., 1996. Genetic architecture of EEG power spectra in early life. Electroencephalogr. Clin. Neurophysiol. 98, 502 – 614. Van Beijsterveldt, C., Molenaar, P.C.M., De Geus, E., Boomsma, D.I., 1996. Heritability of human brain functioning as assessed by electroencephalography (EEG). Am. J. Hum. Genet. 58, 562 – 573. Wijker, W., 1991. ERP Ontogenesis in Childhood. Unpublished Doctoral Thesis. Woestenburg, J.C., Verbaten, M.N., Slangen, L.L., 1983. The removal of the eye movement artifact from the EEG by regression analysis in the frequency domain. Biol. Psychol. 16, 127 – 147.
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Ocular artifacts in children’s EEG: selection is better than correction Riek J.M. Somsen *, Bert van Beek Department of De6elopmental Psychology, Uni6ersity of Amsterdam, Roetersstraat 15, 1018 WB Amsterdam, The Netherlands Received 15 October 1997; received in revised form 26 March 1998; accepted 21 April 1998
Abstract The electroencephalogram (EEG) during middle childhood may be highly distorted by the occurrence of eye and head movement artifacts. Between 5 and 12 years children display a great number of such artifacts. In the present study we studied different methods to assess EEG artifacts in children. Three artifact treatments were compared with the uncorrected EEG: one widely used method which corrected the EEG for electrooculogram (EOG) – EEG transfer and two methods which selected artifact-free EEG segments. The most effective method should selectively reduce the spectral power in the lower frequency bands and at the frontal regions which are most susceptible to eye artifacts. The results demonstrated that the selection procedure, which combined two criteria for the selection of artifact-free EEG segments, was superior. The procedure that corrected the EEG for EOG – EEG transfer unselectively removed spectral power across the whole scalp and across all frequency bands. Furthermore, part of the maturational change in frontal Alpha power was filtered out by the correction procedure. It was concluded that for the background EEG in children, it is better to carefully select artifact-free EEG segments than to correct for EOG – EEG transfer. © 1998 Published by Elsevier Science B.V. All rights reserved. Keywords: EEG artifacts; EEG power spectrum; Children; Background EEG; Absolute power; Coherence
* Corresponding author. E-mail: op – [email protected] 0301-0511/98/$ - see front matter © 1998 Published by Elsevier Science B.V. All rights reserved. PII S0301-0511(98)00041-6
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1. Introduction Brain maturation in children between 0 and about 16 years is marked by specific changes in the electroencephalogram (EEG) (Thatcher, 1991). With age, the amplitude of slow Delta activity between 1.5 and 3.5 Hz and Theta activity between 3.5 and 7.5 Hz decreases and Alpha activity between 7.5 and 12.5 Hz increases (John et al., 1980; Ahn et al., 1980; Anokhin et al., 1996). It is very difficult to obtain reliable EEG records in young children, because their EEG’s usually have a high amount of artifacts. Young children display many eye blinks and other vertical eye movements (VEM), saccades and slow horizontal eye movements (HEM). In addition they often move their heads. Eye blinks cause a typical fast-rising peak in the electrooculogram (EOG) that lasts 70–120 ms. Head movements induce high amplitude EOG distortions which last much longer. These movements also induce distortions in the EEG records, in particular at the frontal channels. From the frontal towards the occipital brain sites the amplitudes of these distortions decrease. One method to remove artifacts, is the submission of EEG segments to an automatic regression procedure that corrects for EOG–EEG transfer (see Gratton et al., 1983; Woestenburg et al., 1983; Gasser et al., 1985; Brunia et al., 1989; Kenemans et al., 1991a,b). EEG correction studies on adult subjects have advocated different solutions for the computation of the regression coefficients. Some authors determine the regression coefficients in the time domain (Gratton et al., 1983), while others have argued that a frequency domain EOG–EEG correction method is superior (see Brunia et al., 1989). From a study in children, Gasser et al. (1985) concluded that not a fraction of the EOG activity is present in the EEG data, but a filtered version of the EOG, with a filter that attenuates the EOG at some frequencies more than at other frequencies. Hence, frequency domain correction was recommended. Frequency domain correction first transforms an EEG segment to the frequency domain and then estimates the vector of coefficients for the regression of EEG on EOG separately for each frequency step. Correction consists of the subtraction of the weighted EOG periodogram from the EEG periodogram (Kenemans et al., 1991b). Next the frequency data can be transformed back to the time domain. Automatic EOG – EEG transfer correction is commonly applied for ERP data (Brunia et al., 1989) and less often for the background EEG. For example, Van Beijsterveldt et al. (1996) and Van Baal et al. (1996) used automatic correction for EOG – EEG transfer on the EEGs of twins in studies on the genetics of the background EEG. Automatic correction implies that the same correction procedure is applied to all EEG records of all subjects and without evaluating the impact of this procedure on each individual EEG record and subject. However, as Gevins (1987) argued, there is no justifiable rationale for the reliance on automated algorithms. The second method to treat artifacts is the selection of EEG segments on the basis of visual inspection after instructions to avoid eye and head movements. This method was used in most early studies on EEG in children (Oken and Chiappa, 1988; Pivik et al., 1993; Anokhin et al., 1996). Visual selection may lead to much
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data loss in young children. Moreover, visually selected EEG may still be contaminated with low amplitude artifacts that cannot be distinguished from the genuine cerebral background EEG. John et al. (1983) reported that slow HEMs and muscle potentials are most commonly missed by artifact detection algorithms. Most EEG artifact studies have paid little attention to the specific characteristics of young children that may enhance the probability of artifacts in their EEGs. The first problem is that the amount of ocular artifact decreases with age. From age 6–7 years, the ability to control ocular movement highly improves. For younger children instructions to avoid eye movements often fail. Hence, until age 6 to 7 many times a very high proportion of the EEG records may be contaminated with eye movements. Moreover, young children usually display many combined eyehead movements (‘looking around’). Head movements induce severe distortions in the EEG which cannot be corrected by regression procedures. Hence EEG segments containing such distortions must be removed from the data-set. This causes a difference in the amount of EEG records that are free of artifacts in children of different ages. The second problem is that ocular and head movement artifacts and maturational changes both occur in the same frequency range from 0–10 Hz (Gasser et al., 1985). Hence, correction for ocular eye artifacts in the background EEG in children may have an unwanted effect on the assessment of the amount of maturational change. Evidently, the reduction in ocular artifacts and the decrease of low frequency EEG power may both be a function of increasing maturation. It is, however, unknown how these two variables are related. The third problem, in children is that the physical distance between the EOG and EEG electrodes is very small. In a study on children of around 10 years, Gasser et al. (1985) concluded that both the EOG and frontal EEG electrodes recorded eye movements as well as cerebral activity. Furthermore, they reported an unexpected high correlation between EEG and EOG power in the Alpha band. The presence of EEG activity in the EOG may lead to confounding effects when correcting for EOG – EEG transfer. The frontal EEG activity in the EOG derivations may ‘correct’ part of the activity in the EEG derivations. Since it is likely that the correlation between EOG and frontal EEG in young children is relatively high, this transfer problem may be much greater in children than in adults. Finally, eye blinks (VEM) may be easy to detect due to their characteristic shape and amplitude, but lower amplitude slow frequency artifacts caused by HEM and genuine cerebral slow activity are difficult to distinguish. This contamination of genuine and artifactual spectral powers is even more problematic in children, because children not only exhibit more artifacts, but also have a relatively higher power in the lowest frequency bands. Since both the EOG and EEG electrodes record the same artifactual and genuine cerebral activity, both the traditional selection by visual inspection and the more recent EOG–EEG transfer correction procedures fail with respect to this problem. From the above list of additional errors that may contaminate the analysis of EEG data in children, it appears that these EEGs require an even more careful procedure to detect, remove and/or correct ocular artifacts. Therefore, we con-
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structed a semi-automatic selection procedure which first detects blinks and other high amplitude distortions in the raw EEG and next defines EEG epochs in segments which do not contain artifactual activity. In addition, we developed a protocol for the selection of EEG epochs that contain only true cerebral activity and no slow and low amplitude artifacts due to saccadic and slow HEM. Our main assumption was that the artifactual activity and the true EEG activity may have different distributions. Most likely, the low frequency cerebral activity will be evenly distributed across the whole EOG time series. Hence, it occurs in all segments of the EEG. In contrast, the low frequency artificial activity will have a periodical on-off character. During an ‘on’ period the artificial activity is added to the cerebral low frequency activity, during an ‘off’ period it is not added to the cerebral activity. When we rank-order the EOG segments on the basis of the amount of power in the lower frequency bands, e.g. 1–6 Hz, the summed cerebral and artificial power will occur in the highest ranks, while the lowest ranks will exhibit only cerebral power. Hence, removal of a percentage of epochs with the highest ranks, will reduce the artificial low amplitude activity, while the true cerebral low frequency activity will remain in the data-set. The above reasoning, resulted in a selection method based on the ranking of epochs on the basis of the amount of low frequency EOG power. This method is similar to an early method used by Mocks and Gasser (1984). It was the purpose of the present study to determine the best artifact treatment procedure for children between 5 and 12 years who took an Eyes Open and Eyes Closed background EEG task. The Eyes Open and Closed tasks are associated with different types of EOG artifacts (Gasser et al., 1985). During Eyes Open VEMs (and head movement due to looking around) are most prominent. During Eyes Closed no blinks, but mainly HEMs occur. The conditions were: (1) all epochs are included; no correction or selection is carried out (RawSignal); (2) automatic EOG – EEG transfer correction of all epochs (correction); (3) detection and removal of epochs with high amplitude artifacts such as blinks and head movements (HighAmp); and (4) the same as (3) with further selection of epochs using a 35% rank-order rejection criterion to remove the EOG epochs with the highest power in the 1 – 6 Hz band (HighLowAmp). To directly compare the contribution of two different selection methods, a fifth condition was added which only removed 50% of the epochs that had the highest EOG power in the 1–6 Hz band (LowAmp). The most effective method of artifact treatment should (Hypothesis 1) reduce low frequency spectral power (Delta and Theta) in the anterior channels (Frontal and to a lesser extend Central) and should (Hypothesis 2) not reduce high frequency power (Alpha and Beta) in anterior, central, or posterior channels. Since, the EEG data of the children show a highly significant decrease with age in the lower frequency bands Delta, Theta and increase with age in the amount of Alpha power, (Somsen et al., 1997), it was hypothesized that Age effects would interact with the Artifact conditions. We expected that the youngest children would profit most from the combined HighLowAmp condition (Hypothesis 3).
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2. Method
2.1. Subjects A total of 142 healthy children (72 girls and 70 boys) from two suburban primary schools served as subjects. There were eight age groups ranging from 5 to 12 years. Boys and girls were approximately equally distributed across the age groups: respectively, (11, 7), (6, 9), (9, 6), (9, 13), (8, 11), (9, 10), (10, 6) and (8, 10) children.
2.2. Physiological recordings The EEG was recorded from 15 channels: F7, Fz, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1 and O2 according to the international 10–20 system, with an electro-cap referred to the right mastoid. Electrode impedance was kept below 8 KOhm. Since there were only 17 polygraph channels available, we used two electrodes for the EOG. These were mounted such that the vertical and HEM were recorded at both electrode locations which was referred to mastoid (Wijker, 1991). One electrode was placed lateral inferior to the canthus of the right eye and the other lateral superior to the canthus of the left eye. Both electrodes recorded vertical and HEM, whereby the same eye movements were reflected in opposite going polarities. These 17 recording channels were amplified and printed on an analogue record using a Nihon-Kohden polygraph. Beckman Ag–Ag/Cl electrodes were used for reference and EOG recording. Data acquisition was under control of a Comtrad-AT interfaced with a Keithley system. EEG and EOG were digitized to A–D values (between 0 and 4096) at 100 Hz during 2 min. Calibration values were recorded once before and once after the experimental tasks. The EOG and EEG data were transformed off-line to mV using the averaged calibration values.
2.3. Recording conditions The children took a neurometric test battery (see Somsen et al. (1990) for a description of this test battery) that consisted of six attentional tasks presented in a fixed order. The first two tasks are reported here. These tasks consisted of 2-min periods of quiet rest with eyes open and eyes closed. The experimental equipment and the child were situated in adjacent rooms. There were two experimenters, one controlled the equipment, visually checked the EEG recordings and initiated the experimental sub-tasks. The second experimenter was sitting behind the child instructing the child to avoid unnecessary body, head and eye movements. Before a task, the experimenter informed the child about the task requirements. The child was sitting in a comfortable reclining chair in front of a Macintosh-Plus computer screen. During the task the child was looking at a dot on the computer display. To reduce test anxiety, children were informed about the experimental procedures by a video film a couple of days before the recording session.
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2.4. Data pre-processing The EEG data were processed by a specially developed program. This program incorporates an earlier brain mapping program (BM) which has been described by Somsen et al. (1994). The data were automatically screened for ‘flat spots’, defined as five or more consecutive points having the same value and for EEG records that had shifted off-scale; i.e. beyond the upper or lower boundaries of the Keithley A/D window. Next the data were calibrated and transformed from A/D units to mVolts. Effects of muscle activity on the EEG from the jaws and neck were screened by a standard deviation criterion in a moving window (4 standard deviations in window of 3 samples). If one of the above described errors was detected in a channel epoch, the epoch for this channel was discarded from the data analysis. Epochs of 2.56 sec that overlapped by 20% were defined in the 2 min EOG/EEG records in 17 channels. First, EOG segments that contained high amplitude artifacts were marked. Next, the EOG/EEG epochs were defined in the unmarked EOG segments. The complete data set was processed four times with different artifact treatments (RawSignal, correction, HighAmp, HighLowAmp). For the RawSignal condition no selection or correction was applied. The HighAmp selection condition consisted of the definition of epochs in EOG segments which did not contain high amplitude distortions. These distortions from eye blinks and head movements were detected using a template matching routine. The amplitude (mV) and duration (ms) of the rising flank of the template could be adjusted to each individual child. Epochs were determined in those parts of the EOG time series that were free of blinks and other high amplitude errors. For the HighLowAmp condition the epochs which remained after HighAmp selection were rank-ordered according to the amount of EOG power in the 1–6 Hz band. The 35% epochs with the highest ranks were removed from the data. It appeared that for some of the younger children too few (less than eight out of the original set of 57) epochs remained in the HighLowAmp condition. For these children the 35% rejection criterion was individually adjusted to a lower percentage, while incidentally artifact-free epochs were defined and added on the basis of visual inspection. To evaluate the impact of the different selection criteria, in one ANOVA HighAmp and HighLowAmp were compared with a fifth selection condition (LowAmp) in which no blink detection was carried out, but in which 50% of all epochs were selected on the basis of the lowest rank of the averaged power in the 0–6 Hz band of the EOG. For the correction condition the ‘matched filter’ procedure described by Brillinger (1975) for EOG – EEG transfer correction in the frequency domain was applied. After Fast Fourier Transformation of the EEG epoch, this procedure determines a gain function which indicates by how much a signal is attenuated or amplified at each frequency and a phase function which gives the delay between the two signals. This results in a vector of complex valued coefficients for each frequency step. Correction consisted of the subtraction of the weighted EOG periodogram from the EEG periodogram.
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A problem, that has attracted little attention in the ERP correction literature is the setting of the frequency range for which the correction for EOG–EEG transfer is carried out. The maximum frequency (Nyquist frequency) is usually set to the maximum frequency in the EEG record (e.g. Gasser et al., 1985; Kenemans et al., 1991b). However, the EOG – EEG transfer artifacts mainly occur at the lowest frequencies. Gasser et al. (1986) concluded that HEM–EEG transfer occurs between 0 – 6 Hz (anterior) and 0 – 4 Hz (posterior), while VEM–EEG transfer occurs between 0 – 8 Hz (anterior) and 0–11 Hz (posterior). Hence, correction beyond these frequencies seems unnecessary and may even lead to the false removal of true cerebral EEG.
2.5. Data analyses After pre-whitening (10% Cosine-Bell window) power spectral analysis on the EOG and EEG epochs using a Fast Fourier Transform routine was carried out. The EEG and EOG were decomposed into the amount of energy (micro Volt squared mV2) at frequency steps of 0.3956 Hz. The lower part of this spectrum (1.5 – 20 Hz) was analyzed. The power spectra were averaged across artifact-free epochs. Smoothing of the spectra was achieved by this averaging (Sturgis, 1983). Broad band frequencies were summed to obtain six indices (absolute power density): Delta (1.5 – 3.5 Hz), Theta (3.5–7.5 Hz), Alpha1 (7.5–10.0 Hz), Alpha2 (10.0 – 12.5 Hz), Beta1 (12.5 – 15.0 Hz) and Beta2 (15.0–19.5 Hz). To achieve normal distributions, all analyzed EEG data were Log10 transformed (Gasser et al., 1985). All figures are based on Log10 absolute power values.
2.6. Statistical tests Repeated measures ANOVA’s (BMDP4V) were carried out with Age Group (8), Artifact Treatment (4) and Region (4) as factors for the Eyes Open and Eyes Closed conditions and for each broad band index. For the Region factor the spectral indices were averaged across the Frontal (F7, Fz, F8), Central and Central/Temporal (T3, C3, Cz, C4, T4), the Parietal and Parietal/Temporal (T5, P3, Pz, P4, T6) and the Occipital electrodes (O1, O2). Hence, the four regions represented increasing distances from the eyes. For the between subject and repeated measures tests the Huynh–Feldt corrected F statistic was used (pB 0.05). Since for each level of the Artifact factor, the same data-set was used (after passing one of the artifact treatments), we preferred to analyze the data as difference scores. Therefore most post-hoc ANOVA’s were carried out on the EEG spectra expressed as difference from the RawSignal condition. Specific post-hoc ANOVA’s were carried out to study small differences between the selection conditions. Different age effects were also explored by comparing the 5, 6, 7 year olds with the 8, 9, 10, 11, 12 year olds and the youngest 5-year-old children with the oldest 12-year-old children. Intra-hemispheric coherency spectra were also analyzed. The selected coherency spectra represented three increasing distances from the frontal electrodes on the
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scalp: (1) averaged coherence between F7 and T3, F7 and C3, F8 and C4, F8 and T4; (2) averaged coherence between F7 and T5, F7 and P3, F8 and P4, F8 and T6; (3) averaged coherence between F7 and O1, F8 and O2. The ANOVA factors for the coherence data were: Age (8), Artifact Condition (4) and Region (3).
3. Results
3.1. Number of artifacts detected Do younger children exhibit more artifacts than older children? The number of epochs that remained after artifact detection by template matching of the EOG (eye-blinks detected in the HighAmp condition) was used as index for the amount of artifacts in the EEG of each child. Simple F-tests were carried out for Eyes Open and Eyes Closed to assess whether the averaged number of selected epochs varied with Age. For Eyes Open the number of selected epochs significantly increased with Age from 19 to 38 epochs (F(7, 129)= 8.48). For Eyes Closed this increase was from 18 to 36 epochs (F(7, 123) = 5.78). Hence, the EEGs of the younger children contained much more eye artifacts than the EEGs of the older children. These results further indicated that the oldest (12-year-old) children still exhibited a substantial amount of artifacts, since only 63% of the epochs (36 out of 57 possible epochs with 20% overlap) were accepted.
3.2. EEG power spectra Are the EEG power spectra differentially influenced by the four artifact treatments? Table 1 provides a summary of ANOVA’s that were conducted to answer this question for the Eyes Closed and Eyes Open task, for each broad band absolute EEG power index. In this set of ANOVA’s the four artifact conditions were included (Table 1). A second set of ANOVA’s on the EEG difference scores (the correction, HighAmp and HighLowAmp data subtracted from the RawSignal data) yielded highly similar effects. These results are not reported separately. The ANOVA’s for each broad band showed highly significant main effects of Artifact and Region. The Artifact main effects confirmed that the different artifact treatments differentially affected the data set. The Region main effects confirmed the well-known decrease of the influence of eye artifacts from frontal to occipital regions. Table 1 summarizes the interactions which include the Artifact factor. The main interaction of interest for this study is the Artifact× Region interaction. This interaction was highly significant for all broad band indices. Furthermore there were significant higher order interactions with Age. These interactions are further analyzed and discussed below. Fig. 1 depicts the Eyes Open, Artifact× Region interactions. It shows the four EEG power band amplitudes averaged across all age groups for each Artifact condition and scalp Region (indicated by the capitals F (frontal), C (Central), P (parietal) and O (occipital) on the X-axis). Inspection of the figure indicates that in
Alpha2
Beta1
* pB0.05, ** pB0.01, Huynh – Feldt adjusted df. All main effects of Artifact and Region are pB0.01.
674.67** (2,239) 644.84** (2,281) 628.01** (3,317) 629.78** (2,287) 1.63* (21,342) 1.94* (9,169) 3.94* (9,157) 1.51 (15,272) 1.67 (13,239) 1.68* (16,280) 2.58** (18,317) 2.11* (16,287)
Alpha1
Eyes Closed: level Artifact×Region (4, 4) 342.87** (3,311) Age×Artifact (8, 4) 1.10 (15,268) Age×Artifact×Region (8, 4, 4) 1.25 (18,311)
Theta
558.21** (3,324) 449.07** (3,317) 478.37** (3,346) 372.53** (2,686) B1 2.16* (13,238) 1.76* (12,227) 1.75* (14,271) 1.34 (17,324) 1.18 (17,317) 2.21** (18,346) 2.75** (15,286)
Delta
Artifact×Region (4, 4) 468.21** (3,351) Age×Artifact (8, 4) 2.18* (13,238) Age×Artifact×Region (8, 4, 4) 1.15 (19,351)
Eyes Open: level
Table 1 Summary of ANOVA’s on EEG power
649.38** (3,318) 1.34 (14,258) 1.87* (18,318)
196.80** (2,242) 1.53 (13,248) 1.97* (13,241)
Beta2
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the RawSignal curves the Frontal Delta and Frontal Theta power amplitudes were increased relative to the more distant C, P and O regions. In the Alpha and Beta bands this difference was absent. Hence, these RawSignal plots indicate that there was additional power at specific data points. The scalp regions and frequency bands of these data points corresponded exactly to the scalp regions and frequencies that were expected to be most sensitive to eye movement artifacts, i.e. the anterior lower frequencies. The figure further indicates that the data points that were relatively increased in the RawSignal curves were not increased in the selection conditions. It is important to note that the RawSignal and the selection conditions concern untransformed EEG data. The difference between the RawSignal and selection signals was that on the basis of certain criteria, only part of the data was included in the selection conditions. Hence, inspection of the curves of these two conditions indicated (a) that the relatively increased Frontal Delta and Frontal Theta data points may have been influenced by artifacts and that (b) that these specific data points changed when only part of the data was selected. We conclude that the results of the two selection conditions both satisfied the predictions of Hypothesis 1 and of Hypothesis 2 that artifact removal should affect only the anterior low frequency bands and that the other frequencies and bands should not be affected.
Fig. 1. Averaged power during Eyes Open in the six broad band parameters (Delta, Theta, Alpha1, Alpha2, Beta1, Beta2) at four regions: F= Frontal, C= Central, P= Parietal and O=Occipital. Each line depicts a data treatment procedure: All data included (RawSignal), EOG – EEG transfer correction (correction), blink detection (HighAmp) and blink detection followed by selection of epochs which have relatively low power in the 1–4 Hz band of the EOG spectrum (HighLowAmp).
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Fig. 1 further illustrates that correction produced a substantial reduction in power amplitude across all broad bands and across all regions. Power reduction was relatively greater at the frontal sites than at the more distant sites. Power reduction occurred both for the slow (Delta, Theta), but also for fast (Alpha, Beta) frequency bands. This latter result was obviously an effect of the conventional setting of the Nyquist frequency to the maximum frequency in the spectrum. This caused an overall reduction of power across the whole frequency range. However, the amount of power reduction was surprisingly high. Hence, it is likely that in addition to the artifactual power, also true EEG power recorded at the EOG electrodes was removed from the EEG in the correction condition. We conclude that the effects of correction were not in accordance with Hypothesis 1 and 2. Do the youngest children profit more from specific artifact treatments than the oldest children? To answer this question, the amount of EEG power reduction relative to the RawSignal was compared between the 5 and 12-year-old children. During Eyes Open the amount of Delta power reduction (Fig. 2, upper panel) in the 5-year-olds appears relatively increased across all four regions. This effect seemed relatively smaller during Eyes Closed (Fig. 3). Since most artifacts were expected at Frontal Delta, a post-hoc ANOVA on the 5 and 12-year-old groups (2) with correction versus HighLowAmp (2) and Region (4) as factors was carried out. This analysis yielded no significant effect for Eyes Closed and a significant Age ×Artifact interaction for Eyes Open: (F(1, 31)= 4.99). Correction reduced the same amount of Delta power in the two groups (5-year-old: 0.28 mV2, 12 year olds: 0.30 mV2). In contrast, the HighLowAmp condition removed more power in the 5-year-old than in the 12-year-old children (respectively, 0.23 and 0.17 mV2). We conclude that when a relatively high amount of artifacts were present (Eyes Open), the youngest children profited more from careful epoch selection than the oldest children. This finding confirmed Hypothesis 3. Fig. 2 indicates that the 5-year-old children showed an small increase in Alpha power after selection at the Central, Parietal and Occipital regions (Fig. 2, upper panel). This increase in Alpha was greater in the 5-year-olds than in the 12-yearolds (F(1, 31) = 6.62). Gasser et al. (1985) also reported that during Eyes Open, Alpha power was higher at epochs without EOG artifacts than at epochs with EOG artifacts. We assume that increased muscle activity during epochs with artifacts in these children may have caused Alpha desynchronization. Hence, when the epochs with increased muscle activity were removed, relatively more Alpha power may have remained in the selected epochs. Hence, this increase in Alpha indicates that more eye muscle artifacts were present in the 5-year-old children. Inspection of Fig. 3 (lower panel) indicates that the correction condition induced increased power reduction in the Alpha1, Alpha2 and Beta1 bands in the oldest children. The increase in power reduction occurred in particular during Eyes Closed (Fig. 3, lower panel). A post-hoc ANOVA with Age 5/Age 12 (2) × Region (4) as factors on the Eyes Closed correction condition showed no Age effects for Delta and Theta, significant main effects of Age in Alpha1 and Alpha2 and significant Age ×Region interactions in the Alpha1 (F(2, 49)=4.54), Alpha2 (F(2, 50)= 7.83),
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Fig. 2. Averaged power during Eyes Open in the 5-year-old children (upper panel) and in the 12-year-old children (lower panel). The amount of power reduction relative to the RawSignal condition is depicted for the correction, HighAmp and HighLowAmp conditions and for the Delta, Theta, Alpha1, Alpha2 and Beta1 broad band parameters at the frontal, central, parietal and occipital regions.
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Fig. 3. Averaged power during Eyes Closed in the 5-year-old children (upper panel) and in the 12-year-old children (lower panel). The amount of power reduction relative to the RawSignal condition is depicted for the correction, HighAmp and HighLowAmp conditions and for the Delta, Theta, Alpha1, Alpha2 and Beta1 broad band parameters at the frontal, central, parietal and occipital regions.
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Beta1 (F(1, 43) = 9.35), Beta2 (F(1, 36)= 4.91) bands. In these higher frequency broad band indices, relatively more power reduction occurred in the 12-year-old children. Power reduction was relatively increased at the Frontal and Occipital sites of the Alpha1 band (see Fig. 3). A study of Somsen et al. (1997) on this same dataset reported that Occipital Alpha increased with age and that Frontal Alpha power increased between 10 and 12 year olds in particular during Eyes Closed. Hence, this result indicates that part of this maturational change was filtered out by the correction condition. We assume that Eyes Open is more prone to artifacts than Eyes Closed. Hence the selection conditions should differentiate between these two tasks. A post-hoc ANOVA was carried out on the Delta band (difference from RawSignal) for the Eyes Open and Eyes Closed selection conditions. An ANOVA was carried out with all Age groups (8), the Eyes Open and Closed Tasks (2), the Frontal and Central Regions (2) and the HighAmp and HighLowAmp selection conditions (2) as factors. The results showed highly significant main effects of Task (F(1, 116)= 145.10), Region (F(1, 116) =514.34) and selection condition (F(1, 116)=136.97). The HighLowAmp condition induced more power reduction than the HighAmp condition (respectively, 0.20 and 0.17 mV2). The highest reduction occurred in the HighLowAmp condition at the Frontal region. A significant interaction of Task× Region (F(1, 116) = 187.39) confirmed that Frontal Delta reduction was much higher for Eyes Open (0.37 mV2) than for Eyes Closed (0.15 mV2) while Central Delta reduction was less different between the two tasks (respectively, 0.14 and 0.7 mV2) (see Fig. 4). Age group showed a marginally significant main effect (F(7, 116) = 1.97, p B0.07). Fig. 4 suggests that the amount of Delta power reduction initially increased from the 5 to the 7-year-olds and then decreased between 7 and 8 years, but did not decrease further with increasing age. To further analyze the difference between 5–7 and 8–12 years in relation to the different selection conditions, the subjects were split into a younger (age 5, 6, 7 year) and older Age group (8, 9, 10, 11, 12 year). The three selection conditions: HighAmp, HighLowAmp and LowAmp were compared. Power values were expressed as difference from RawSignal. Only the Frontal and Occipital regions were compared. Hence, the factors in the ANOVA’s were: Age (2)× Artifact (3)× Region (2). The results for the Delta band showed highly significant main effects of Age (Eyes Open: F(1, 134) =11.61; Eyes Closed: F(1, 134)=11.61) and significant interactions of Age× Artifact ×Region (Eyes Open: F(2, 380)= 8.82; Eyes Closed: F(3, 408) = 2.83). These effects were also significant for the Theta band, but not for the Alpha and Beta bands. Fig. 5 illustrates the Eyes Open results. The reduction in power induced by the selection conditions was highest in the Delta band and at the Frontal region. The combined selection condition (HighLowAmp) showed the greatest power reduction for Delta in the youngest children. The LowAmp condition induced power reduction that was similar, but somewhat less than the two other selection conditions. Recall that LowAmp was based on the removal of 50% of epochs with greatest amount of low frequency EOG activity. This 50% criterion
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Fig. 4. Comparison of the amount of power reduction (from the RawSignal condition) in the Delta band between the HighAmp and HighLowAmp selection conditions and between the Eyes Open and Eyes Closed tasks at the Frontal and Central regions for eight Age groups.
was arbritarily chosen. The interactions supported our hypothesis that the combined VEM (HighAmp, blink) and HEM (LowAmp, low frequency) selection methods would be most effective. The combination of High and LowAmp selection specifically removed most power in the frontal Delta band of the youngest children.
3.3. Coherence results Do the four artifact treatments differentially affect the EEG Coherence spectra? The ANOVA’s with Age (8), Artifact Treatment (4) and Region (3) as factors on the coherence data showed highly significant main effects of Artifact and Region and significant interactions of Age group× Region and of Age × Artifact and of Artifact ×Region. During Eyes Closed the Alpha1 (F(19, 342)= 1.99 pB 0.008) and Alpha2 (F(20, 353) =1.91 pB0.01) indices showed significant higher order interactions with Age. The main result of the coherence analyses was an Artifact× Region interaction which is illustrated in Fig. 6 for Alpha1 Eyes Open (F(3, 367)= 187.90) and Eyes Closed (F(3, 342)= 150.63). The Figure shows that coherence was systematically lower in the correction condition as compared with the other three conditions. Fig. 6 also illustrates the interaction with Region when the distance between electrode pairs increased, the additional coherence reduction decreased. We conclude that correction systematically depressed intra-hemispheric coherence.
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4. Discussion Four different artifact treatments for EEG power spectra in young children between 5 and 12 years with a high percentage of artifacts were compared. In young children reliable EEG measurement is problematic, because children of this age have difficulty to control their eye and head movements after instructions to do so. This was confirmed by the high amount of detected EOG artifacts. The amount of artifacts significantly decreased with age. The plotted RawSignal curves indicated the data points that were most susceptible to artifactual influences on the background EEG, power values were clearly elevated in the Frontal Delta and Frontal Theta measures. This result was consistent with our Hypothesis 1 which predicted that the low frequency Frontal and Central regions would be most influenced by eye movement artifacts. The RawSignal Alpha and Beta measures did not exhibit such elevations. Moreover, the curves of the selection conditions did not show elevations in Frontal Delta and Theta. This difference supported our reasoning that a selected part of the EEG epochs may display the true cerebral EEG variance equally well as the complete set of epochs. However, the complete set displayed additional artifactual variance.
Fig. 5. Comparison of the amount of power reduction (from the RawSignal condition) in the youngest (first triple of bars) and oldest (second triple of bars) children between three selection procedures: HighAmp, HighLowAmp and LowAmp during Eyes Open. From left to right on the X-axis the clusters of six bars represent the amount of power reduction in Frontal Delta, Occipital Delta, Frontal Theta, Occipital Theta, Frontal Alpha1 and Occipital Alpha1.
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Fig. 6. Averaged coherence spectra in the RawSignal, correction, HighAmp and HighLowAmp conditions in the Alpha1 band during Eyes Open (left) and Eyes Closed (right). The coherences between the Frontal-Central, Frontal-Parietal and Frontal-Occipital regions are depicted on the X-axis for each artifact treatment condition.
Comparison of the different artifact treatments strongly indicated that selection of EOG artifact-free epochs is better than correction for EOG–EEG transfer. Relative to the RawSignal, the selection conditions induced only minor changes in power at the Alpha and Beta bands. This absence of change was predicted by Hypothesis 2. In contrast, correction induced reductions in power in all broad bands and at all regions. Power reduction at the Frontal regions was much higher than at the other regions. Also Alpha and Beta power were substantially reduced. This result is consistent with results of Gasser et al. (1986) who reported that correction reduced the higher frequencies. Obviously, this global effect of correction could have been partly prevented by using a smaller frequency window (Nyquist frequency). However, restriction to the Delta and Theta window would not solve the problem of over-correction. Fig. 1 illustrates that over-correction occurred at the Frontal sites of all broad band indices. This result raises the question why Frontal power reduction was so much higher for correction as compared with selection. We suggest that this may have been caused by the frontal EEG activity that was recorded by the EOG electrodes. It seems that the correction procedure not only removed shared artificial activity between the EOG and the EEG sites on the scalp, but also shared normal Frontal EEG activity. Also the increased removal of Frontal and to a lesser extend
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Occipital Alpha in the 12-year-old children suggests that normal maturational increase in power was partly removed by correction. This suggestion is supported by the coherence results. In the correction condition, the coherence between the F–C, F – P and F – O channels was much smaller than in the other conditions. Moreover, the region which showed the highest coherence (F–C) reduction also showed the greatest over-reduction of power. Since coherence after HighAmp and HighLowAmp did not differ much from the RawSignal coherence, we conclude that selection did not change the normal EEG coherence activity. Selection of artifact-free EEG epochs reduced more power in the Eyes Open task than in the Eyes Closed task. Moreover, power reduction during Eyes Open of Frontal Delta activity was much greater than of Central Delta activity. These findings are consistent with the assumption that an Eyes Open task is more prone to artifacts than an Eyes Closed task. During Eyes Closed, there is no visual input which largely prevents the occurrence of blinks and combined eye–head movements. During Eyes Open, HighLowAmp selection induced additional Frontal Delta reduction as compared with HighAmp. Also, the expectation that the youngest children who produced most artifacts, would benefit most from the double selection procedure was confirmed. In Fig. 5 the 5–7-year-olds showed relatively increased power reduction due to HighLowAmp compared with the 8–12-year-old children. This difference did not occur for the Theta band or for any of the other high frequency bands. Hence, this result indicated that the effect of the combined strategy was highly specific. The additional power reduction was restricted to, Eyes Open, 5 – 7-year-olds, Frontal Delta. Slow HEMs due to looking around, seems the most probable source for this additional power. Finally, it appeared that the HighAmp and LowAmp procedures affected common sources of artifacts. HighAmp was based on the concrete detection of eye blinks, whereas LowAmp was based on a general criterion to remove EOG epochs with the highest low frequency power. The setting of this criterion was arbitrary and did not vary between subjects. Apparently, blinks and head movements add low frequency power to the EOG and EEG spectra. Power variance due to artifacts is added to the variance of the cortical sources. These two sources of variance may be distinguished by the different nature of their distributions across the EEG record. We conclude that the avoiding of EEG segments with eye blinks and the subsequent removal of epochs on the basis of the more robust LowAmp procedure is an adequate strategy to prevent that the EEG is contaminated with eye and head movement artifacts. The present results indicated that the automatic correction of EEG artifacts should be avoided, because this may have undesirable effects on the EEG data. For example, in the twin studies of Van Baal et al. (1996) and Van Beijsterveldt et al. (1996) correction for EOG – EEG transfer of the background EEG was applied. In the youngest twins automatic correction followed the removal of epochs with artifacts on the basis of visual inspection (Van Baal et al., 1996). Their results showed that the correlations and heritabilities were relatively lower in the Frontal Delta band than in the Theta, Alpha and Beta bands. The authors attributed this
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finding to the relatively increased contribution of eye movements in the Delta band. The present data showed that correction produces over-correction of Frontal Delta. Hence, we suspect that not the eye movements itself, but the correction method that was chosen to remove the eye movements may have negatively influenced the results in these studies. With respect to ERP data, it is, as yet, uncertain whether correction has any negative influence on the ERPs of young children. We assume that this may depend on the extend to which task relevant cerebral activity is removed after correction. From the current results, we suggest, that both EEG and ERP data measured in young children require a very careful artifact removal procedure.
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