Muscle artifacts constitute one of the major problems in electroencephalogram (EEG) examinations, particularly for the diagnosis of epilepsy, where pathological rhythms occur within the same frequency bands as those of artifacts. This paper proposes to use the method dual adaptive filtering by optimal projection (DAFOP) to automatically remove artifacts while preserving true cerebral signals. DAFOP is a two-step method. The first step consists in applying the common spatial pattern (CSP) method to two frequency windows to identify the slowest components which will be considered as cerebral sources. The two frequency windows are defined by optimizing convolutional filters. The second step consists in using a regression method to reconstruct the signal independently within various frequency windows. This method was evaluated by two neurologists on a selection of 114 pages with muscle artifacts, from 20 clinical recordings of awake and sleeping adults, subject to pathological signals and epileptic seizures. A blind comparison was then conducted with the canonical correlation analysis (CCA) method and conventional low-pass filtering at 30 Hz. The filtering rate was 84.3% for muscle artifacts with a 6.4% reduction of cerebral signals even for the fastest waves. DAFOP was found to be significantly more efficient than CCA and 30 Hz filters. The DAFOP method is fast and automatic and can be easily used in clinical EEG recordings.
Electroencephalograms (EEG) remain essential in neurological practice; their indications are even increasing, especially for long-term EEG. EEG are captured continuously, sometimes during several days for hospitalized patient or for outpatients, in order to record paroxysmal clinical manifestations. EEG interpretation is difficult due to the low signal quality, specifically due to the numerous muscle artifacts interfering with the paroxysmal abnormalities detection or with the seizure analysis. Filters distributed with commercially available devices are insufficient. Either they do not eliminate enough muscle signal or they alter dramatically the cerebral signal. New automated filters are then required to better eliminate muscle artifacts, without altering cerebral signals.
Artifacts can have other origins including power source, eye movement/blinking, electrode, galvanic sudation, chewing, and heartbeat. This paper focuses on muscular contractions, which are the most important sources of artifacts under certain recording conditions. Muscle artifacts correspond to the electromyographic (EMG) potentials generated mainly by jaw and forehead muscles. For this reason, they are generally more important on the temporal and frontal channels. The major part of the signal power occurs at high frequencies(
Frequency decomposition of cerebral rhythms and muscle artifacts [
The challenge for neurologists is to analyze brain signals masked by the artifacts in order to diagnose underlying pathologies. Brain signals measured on the scalp surface can be classified into four main frequency bands:
Although muscle artifacts are faster than EEG signals, there is some overlap in the frequency domain, particularly with pathological signals. Therefore, conventional digital filters cannot be used to remove artifacts without distorting the cerebral activity. An attractive solution is spatial filtering based on regression methods (for review see [
In this paper, dual adaptive filtering by optimal projection (DAFOP) is proposed to filter muscle artifacts. The DAFOP method was introduced in our previous work [ Subjects do not have to perform the prerecording of two minutes at the beginning of each session to detect the spatial localization of the artifacts. The level of filtering adapt as a function of the number and amplitude of artifacts. Thus, DAFOP does not remove signals when there is no artifact. DAFOP is not limited on the number of possible artifact sources, contrary to AFOP which filters only the artifact sources experienced during the training period.
DAFOP combines spatial and frequency filtering. The principle consists in comparing two frequency windows with common spatial pattern (CSP) in order to identify brain sources using an a priori defined frequency power distribution. The entire EEG is then independently rebuilt by applying a regression method to various frequency windows. Because the optimal choice of frequency windows is problematic, a semiautomatic process is proposed to obtain the best settings. DAFOP is then evaluated through visual analysis of clinical EEGs and compared to two other methods: BSS-CCA [
Let
As in all methods of spatial filtering,
Artifact and cerebral signals are not always activated together and some may only belong to a specific frequency window. Since, in practice, the number of artifact and cerebral sources is far superior to the number of channels, attempting to filter an artifact which is not actually present leads to a small diminution in cerebral signal and trying to maintain a weak cerebral signal leads to the maintaining of a small portion of artifacts. In previous works [
Signal decomposition consists in working within a temporal sliding window (corresponding to the matrix
The construction of construction of construction of
This first step is common to all frequency windows inside a temporal sliding window
A frequency pattern is defined by two frequency windows
The cerebral component separation
The second step of DAFOP consists in determining the mixing subspace of cerebral sources using a linear regression method on
The aim of the regression is to find the mixing matrix
BSS-CCA is another method to filter muscular artifacts [
The BSS-CCA algorithm aims to find the most autocorrelated sources. For a discrete signal
If we neglect border effects by considering
The two frequency windows used for DAFOP optimization in order to have CCA equivalence. The windows take into account the preprocessing of the recording proposed in [
The equivalence between (
The BSS-CCA method is an efficient method to filter muscle artifacts. However, it would be possible to use the frequency decomposition in order to remove more components from artifacted frequencies and to keep more components in nonartifacted frequencies. In addition, it would be reasonable to hypothesize that setting
In order to apply DAFOP to muscle artifacts, it was necessary to define a set of parameters, namely: frequency window decomposition the two frequency windows the number of components to conserve within each window
Figure
Steps of the DAFOP method to filter muscle artifacts.
To set these parameters, a training dataset of 12 clinical EEG recordings of different patients was selected. Recordings were performed at the Hospital Group GHICL (Groupe Hospitalier de l’Institut Catholique de Lille) and the Hospital Center of Lille, France, using Nihon Kodhen, Nicolet, and Micromed devices. The electrodes were positioned according to the 10/20 system with 19 electrodes. Preprocessing consisted of a common mean reference, a high-pass filter at 0.5 Hz, a low-pass filter at 70 Hz, and a notch filter at 48–52 Hz (power line frequency). These filters were 6-order Butterworth (12 for the notch) applied with a forward-backward process to prevent phase shifting. Recordings included, among other signals, some epilepsy seizures, and pathological rhythms (spikes, spike waves, etc.). Several trials were run on this dataset to adjust the various parameters. The setting was done either subjectively or by optimization, in order to best remove muscle artifacts without erasing cerebral rhythms. The next paragraphs explain these choices.
Frequency decomposition
No muscle artifacts are present in the frequency band 0–8 Hz. Consequently, it is not necessary to remove components and the signals can remain unchanged. For the other bands, the artifact ratio increased with frequency (Figure
As shown in Figure
The signals within the frequency band 0 to 8 Hz are not changed at all since there are no muscle artifacts in this band. Consequently, cerebral sources carrying theta and delta waves are not needed.
Taking into account these observations, an initial empirical choice could be
The selected periods last about 20 s by signal. In order to represent the wide variety of possible cerebral signals and muscular artifacts, approximately 100 periods were included in the selection for each signal. At the end, two signals of approximately 30 min duration each were obtained. Figure
Smoothed Fourier transform module for the two training signals.
The two signals are then preprocessed using a high-pass filter with a cutoff at 8 Hz so signals (artifact and cerebral) below these bands would not interfere with the filter settings. Indeed, most muscle artifacts are associated with electrode artifacts since the muscle contraction drives a facial movement. Nevertheless, the electrode artifact distribution is not directly linked to the electromyographic artifact distribution. It is therefore important to ignore the electrode artifact when determining muscle artifact distributions.
Due to frequency preprocessing of
Frequency windows of DAFOP filtering obtained by FIR optimization.
The last parameter to set is the number of conserved components
Furthermore, since there are more artifacts at high frequencies, the number of removed components should increase with frequency. This is why the threshold
Ideally, a component should be removed when it contains more than a certain percentage of artifacts (around
An evaluation by an expert neurologist was conducted in order to compare the results with other methods described in the literature and to evaluate the results in using this method in routine clinical practice.
Clinical recordings of 20 epileptic patients with pertinent cerebral rhythms were selected. These recordings were different from those of the training dataset but were acquired under the same conditions. They lasted from 20 minutes (short duration recordings) to 4 days (long duration recordings). Firstly, 114 relevant pages were selected among the 20 recordings without viewing the filtered result. One page corresponded to a 20 s epoch of EEG with the 19 channels according to the 10/20 system. The selection was done with respect to various levels of artifact power and for a wide variety of cerebral signals. Coauthor neurologists selected EEG pages from their own patients. Selected pages were anonymized. As such, no specific assessment was necessary.
The three following filters were then compared: a standard 1-order low-pass filter at 30 Hz common to many EEG device software applications, a filter achieved with BSS-CCA [ a DAFOP filter with the above optimized parameters.
For this study, we used a previously published program (
Two expert neurologists compared the filtered signals of these EEG pages. For each page, the filtering results of the three methods were presented at random, so experts performed a blinded analysis, thus reducing subjectivity.
Each expert analyzed one half of the dataset. For each page, the raw recording was first interpreted. The experts evaluated the presence of cerebral activities with the following categories (spikes, spike waves, alpha, pathological rhythmic discharges, spindles, and vertex sharp waves). In addition, they examined the original amount of muscle artifacts and scored it from 0 to 4 (0 = no artifact; 4 = very high level of artifacts).
Thereafter, the experts analyzed the results of each filtering method. They scored the proportion of removed muscle artifacts from 0 to 4 for (0 = no amplitude reduction (
Finally, experts have balanced all criteria to determine for each page the most efficient method. Balancing takes into account the muscle artifact elimination, the proportion of reduced cerebral activities, and the artifact addition or modification. Since the signal belonging to the frequency band 0–8 Hz was not modified, the slow waves were not reduced. Consequently, the reduction was not subject to evaluation. However, experts could consider a given page to be better if the slow waves were more visible after filtering.
Statistical sign tests were done to compare CCA and DAFOP for each parameter and for all balanced criteria comparison. The
Table
Estimation of average ratios of artifact/cerebral signal elimination.
DAFOP | CCA | 30 Hz | Studied pages/signals | |
---|---|---|---|---|
Estimation of the average ratio of cerebral signal elimination | ||||
Alpha rhythm | 5.74% | 11.30% | 5.25% | 71 |
Epileptic rhythmic discharge | 8.50% | 14.50% | 5.00% | 15 |
Spike waves | 5.51% | 11.47% | 5.00% | 34 |
Spikes | 7.50% | 7.70% | 5.00% | 49 |
Spindles/vertex spikes | 5.00% | 5.00% | 5.00% |
|
Global |
|
|
|
|
Estimation of the average ratio of electromyographic artifact elimination | 84.29% | 82.28% | 55.51% | 108 |
Amount of removed cerebral signals per 20 s page (0: no difference (
Regarding preservation of cerebral activity, the 30 Hz filter had the best performance (elimination of 5.1% of cerebral activity), followed by DAFOP (6.45%) and lastly by CCA (10.58%). For CCA, these differences were pronounced for alpha rhythms, epileptic rhythmic discharges, and spike waves. Figure
Concerning elimination of electromyographic artifacts, DAFOP performed the best (84.29% elimination), then CCA (82.28%), and lastly the 30 Hz filter (55.51%) which is clearly less efficient than the first two methods. However, in the four cases analyzed with DAFOP and the case analyzed with CCA (none for 30 Hz filter), an added or transformed artifact could have been interpreted as a pathological signal, if the filtered EEG was analyzed alone. Moreover, on 10% of pages for DAFOP and 3% for CCA, the muscle artifact residue could be confounded with alpha rhythm.
As on the original assumption, the experts did not notice any significant differences on the delta and theta rhythms, in the 23 concerned pages.
Figure
Distribution of the estimated amount of removed artifacts versus artifact amplitude for each method.
Table
Blinded expert comparison of DAFOP and CCA filtering.
Comparison of | Sign test | Details |
|
Conclusion | Significance |
---|---|---|---|---|---|
Electromyogram elimination (all levels of artifacts) | Two-sided | DAFOP is better on 31 pages; CCA is better on 15 pages; same level on 67 pages. |
|
DAFOP has more often better electromyogriam elimination | Significant |
Cerebral signal elimination (all cerebral signals of the study) | Two-sided | DAFOP is better on 30 signals, CCA is better on 7 signals; same level on 142 signals. |
|
DAFOP has more often better cerebral signal conservation | Highly significant |
Alpha rhythm elimination | Two-sided | DAFOP is better on 12 signals, CCA is better on 1 signal; same level on 58 signals. |
|
DAFOP has more often better Alpha rhythm conservation | Highly significant |
Spike elimination | Two-sided | DAFOP is better on 5 signals, CCA is better on 6 signals; same level on 38 signals. |
|
DAFOP and CCA spike conservation seem similar | Not significant |
Spike-wave elimination | Two-sided | DAFOP is better on 10 signals, CCA is better on 0 signal; same level on 24 signals. |
|
DAFOP has more often better spike-wave conservation | Highly significant |
Epileptic rhythmic |
Two-sided | DAFOP is better on 3 signals, CCA is better on 0 signals; same level on 12 signals. |
|
DAFOP has better epileptic rhythmic discharge conservation | Not significant |
|
|||||
Quality of filtering by pages, all criteria balanced | One sided | DAFOP is better on 58 pages; CCA is better on 33 pages; same quality on 23 pages. |
|
DAFOP is more often judged better than CCA when balancing all criteria | Highly Significant |
The statistical analysis shows that DAFOP was globally more efficient concerning both electromyographic artifact (
An example of the results obtained with the three methods is given in Figures
Example of Raw EEG signal with important muscle artifact (level 3/4), including α rhythm and spikes (F7, T3, T5).
Filtering result with DAFOP method.
Filtering result with CCA method.
Filtering result with a 30 Hz filtering.
DAFOP and CCA both gave promising results for the elimination of electromyographic artifacts on EEG recordings and both offer a high selectivity concerning the conservation of normal and pathological cerebral signals. On average, the filtering rate was 84.3% and 82.3%, respectively, for muscle artifacts whereas for cerebral signals it was 5.7% for DAFOP and 11.3% for CCA (Table
For the three methods, the reduction scored by neurologist always corresponded to an amplitude reduction of signals and never to a signal deformation. However, it can be noticed, however, that for the 30 Hz filter and the DAFOP filter the high frequencies can be a bit more reduced than the low frequencies. Consequently a spike wave will have his spike slightly more reduced than the wave. Nevertheless, this effect was very low and it would be difficult to quantify it visually.
DAFOP transformed, in some less frequent cases, artifacts into signals which could be misinterpreted as cerebral signals by inexperienced readers. Artifact transformation also arises with CCA but to a lesser extent. Although the DAFOP filter could be placed at a stronger setting to remove those signal addition/transformation, our priority was to optimize conservation of cerebral signals. Taking into account that both unfiltered and filtered signals are analyzed by physicians, signal addition does not represent a real life limitation and as such the proposed settings are optimized with these results in mind.
Regarding electromyographic elimination, the efficiency of both DAFOP and CCA is almost independent of the amount of artifacts (Figure
The 30 Hz low-pass filter is a conventional filter used in clinical practice but is inefficient in the presence of important artifacts (Figures
Statistical comparison between DAFOP and CCA demonstrates that DAFOP method is better in conserving cerebral rhythms, particularly alpha rhythms and spike waves. DAFOP has also better achievement in electromyogram artifact elimination. Consequently even if the threshold of CCA would be changed, DAFOP will have a better selectivity. Finally, the general comparison of methods proves that even if there can be artifact addition/modification, DAFOP overall gives better results than CCA.
Concerning the method functioning, DAFOP and CCA are both based on the separation of components which optimize frequency pattern and this seems to be an efficient criterion. However, the CCA frequency patterns are not directly optimized for the problem but result from methodological simplification which could explain partially the better results of DAFOP. The other explanation for the DAFOP superiority is frequency decomposition which increases the possibility of source separation. Using CCA with this frequency decomposition should give also good results.
It would be interesting to compare the DAFOP method with other methods referenced in the literature like standard AFOP [
In relation to standard AFOP, according to the parameters and the results in previous studies [
In regard to ICA, we have previously done a comparison between standard AFOP and manual ICA [
Most of the time only first order filters are implemented in EEG reading devices and this is why we decided to compare with this. Higher order filter would probably be more efficient. However, due to the frequency distribution overlap between muscular artifacts and spikes (Figure
There are very few papers which demonstrate statistically that a filtering method is better than another [
The main potential bias of this study is that two experts do not represent the large variety of neurologist experts, and their opinions can be different from the reality. In addition, each of them only analyzed half of the database. However, since the significance is reached it will not change the conclusion and it will not remove the bias that there is only two experts. Nevertheless, it would be interesting to perform an interobservator comparison and verify the expert concordance. Such study has been realized in [
Future studies should be done to complete these results by a qualitative and objective comparison on a synthetic signal, where the true cerebral signal is known. Some authors [ It would not be possible to have a perfect unartifacted signal and a perfect muscle artifact source. Considering the artifact as a mixing of limited number of sources is not realistic for an important artifact. This model would ease the problem and particularly it would render the frequency decomposition process almost useless.
Despite these two problems, it would be interesting to verify that the conclusions are the same with such model.
Finally, this evaluation method mainly concerns visual examination of EEG, but it can be supposed that if the method is applied as preprocessing for other applications like source localization, anticipated detection of epilepsy seizures, brain computer interfaces, the optimal parameters, and the evaluation could be different.
From a practical point of view, all three methods are entirely automated. A turn of a switch moves from raw recording to filtered recording. It is not the case of methods like ICA [
The methods can be applied to any EEG recording and do not require additional electrodes like regression methods [
In addition, all three methods have a very short computation time (
An advantage for DAFOP and to a lesser extent for CCA is that methods are stable if there is a disconnected or a missconnected electrode; that is, this electrode artifact will not be removed but the other channels will be still well filtered and the artifact will not be propagated on other channels. This is due to the fact that those kinds of signals are always uncorrelated to other channels on the concerned frequencies and the frequency pattern is very different of a muscular artifact. Sometimes, a power line artifact residue or another high frequency intrinsic instrument noise appears on an EEG channel. Even if the frequency pattern does not perfectly match, the power ratio
Those advantages are particularly important on the context of standard EEG examination where recording have to be quickly analyzed.
This paper describes the use of the DAFOP method to filter muscle artifacts on EEG recordings and discusses optimization of the method. DAFOP was evaluated on clinical EEG recordings by two neurologists and compared with BSS-CCA and the 30 Hz low-pass filter. DAFOP was particularly efficient for artifact removal (84% on average) while offering very good conservation of cerebral signals (6.4% reduction on average), particularly pathological signals. Comparison with the 30 Hz filter commonly used in routine practice showed that the latter is far less efficient than DAFOP and BSS-CCA in enhancing EEG readability. In comparison with BSS-CCA, DAFOP was judged globally to be more efficient.
In addition to improving EEG readability, this method overcomes three drawbacks commonly reported in the literature [ It does not require manual intervention. It has a low computational time enabling a neurologist to visualize for each second, 1 min of filtered EEG without previously processing the data. It works on any clinical EEG recording device without modifying current practice (no additional electrodes needed [
The method can also be combined with other methods to filter all types of artifacts. For example, DAFOP can be combined with AFOP [
This paper presents the using of DAFOP on the clinical context of EEG examination. Thus the method is parameterized and evaluated on this context. There would be many other applications of this filtering which probably require some small adjustments. For example, if the aim is to study the Fourier transform of EEG signal, the frequency decomposition step would add discontinuity on the gain multiplier. The Fourier transform of cerebral signal is already very discontinue and fortunately the discontinuity cannot be seen unless we observe the mean of several EEG spectra. The method should also be set and tested on other devices like magnetoencephalogram (MEG) and on EEG with more electrodes. Some adjustments should also be done for recording on countries with 60 Hz power line frequency. Finally, it would also be interesting to apply this method as preprocessing for other applications such as source localization, brain computer interfaces, and anticipated detection of epilepsy seizure [
For now, the possibility of implementing this method on clinical EEG devices is being studied.
The authors declare that there is no conflict of interests regarding the publication of this paper.
The authors thank David Perry from Hautes Etudes d’Ingenieurs and Amelie Lansiaux from GHICL for their kind critical reviewing.