=Paper=
{{Paper
|id=Vol-2843/shortpaper009
|storemode=property
|title=The algorithm of automatic localization of EOG artifacts in a multichannel EEG signal (short paper)
|pdfUrl=https://ceur-ws.org/Vol-2843/shortpaper009.pdf
|volume=Vol-2843
|authors=Natalya Bodrina,Konstantin Sidorov
}}
==The algorithm of automatic localization of EOG artifacts in a multichannel EEG signal (short paper)==
https://ceur-ws.org/Vol-2843/shortpaper009.pdf
The algorithm of automatic localization of EOG artifacts
in a multichannel EEG signal1
Natalya Bodrina and Konstantin Sidorov
Tver State Technical University, 25, Lenina Ave., Tver 170023, Russia
vavilovani@mail.ru
Abstract. The problem discussed in the paper is automatic localization of ab-
normal areas (artifacts) of the electroencephalogram (EEG), which are recorded
at the moments of time when physiological disturbances caused by eye move-
ment and blinking occur (electrooculogram, EOG). The authors propose a new
algorithm for automatic search and removal of electrooculogram artifacts. The
base of the algorithm is the relationship between the standard deviations of the
electroencephalogram eye lead amplitudes (Fp1-A1 and Fp2-A2). The standard
deviations of every epoch in eye lead are compared with the mean standard de-
viation over this lead. The epochs with localized EOG artifacts are removed in
all EEG leads. The algorithm is implemented in MATLAB. The program allows
obtaining data on the artifacts detected in an EEG recording: number, localiza-
tion by epochs, a reconstructed purified electroencephalogram and remote ar-
eas. The algorithm was tested on 600 EEG samples collected during experi-
ments in the Tver State Technical University. The participants in the experi-
ments were 20 people. In the received EEG records the experts identified areas
containing electrooculogram artifacts of different activity. The results of the re-
search on the algorithm operation using the examples of electroencephalogram
recordings have shown its practical effectiveness.
Keywords: electroencephalogram, electrooculogram, lead, epoch, artifact, lo-
calization, algorithm.
1 Introduction
When recording an electroencephalogram (EEG), artifacts of a various nature arise.
These are records of extraneous processes that are not direct evidence of the brain
electrical activity [1]. In this regard, EEG signals demand preliminary processing.
We divide artifacts into two groups by origin [1- 2], which are physical (hardware)
and biological (physiological). Physical artifacts appear due to violating the equip-
ment technical regulations and EEG recording rules, as well as due to the equipment
imperfection. The cause of appearing physiological artifacts is additional recording of
1
Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribu-
tion 4.0 International (CC BY 4.0).
the functional activity of organs and systems of the body in addition to the brain. The
reasons might be the evoked potentials of blinking and eye movement (electrooculo-
gram, EOG), muscle contractions (electromyogram), muscles and the conducting
system of the heart (electrocardiogram), galvanic skin reflexes, swallowing move-
ments.
There is an intensive development [1-2] in the field of solving problems related to
localization of different types of artifacts in EEG signals. In particular, various
mathematical methods of analysis are used to efficiently localize and remove artifact
EOG patterns. There are various approaches based on frequency filtering [3-4], a
regression analysis [3; 14], a wavelet analysis [4; 11-13], a principal component
analysis (PCA) [5], an independent component analysis (ICA) [6-8], artifact subspace
reconstruction (ASR) [7], a correlation analysis [7; 15], a neural network approach [9;
10], a variational mode decomposition [16], sparsity-based techniques [17], etc. All
these approaches have their own advantages and disadvantages, thus the problem of
localization of EOG artifacts has yet to been solved.
The paper proposes a new algorithm that allows automatic localization of EOG
electrooculogram artifacts in a multichannel EEG signal.
2 Materials and Methods
The algorithm is based on the correlation of the standard deviations of the amplitudes
for each EEG eye lead (Figure 1). For each eye lead (time series (TS)), using the cal-
culation window d with the length equal to one epoch (d = 250 readings, 1 sec), the
TS amplitude standard deviation (SD) is calculated:
N
SD( j ) N 1 ( xl x) 2 (1)
i 1
Where SD(j) is SD for the j-th epoch; xl is the l-th element of the j-th epoch;
l 1, N ; N is the total number of elements in the j-th epoch; x is the arithmetical
mean of the j-th epoch.
Then the calculation window moves to the right by its own length and the feature
calculation is repeated. The SD(j) feature estimates are compared with the estimate of
the mean SD over the entire lead (SD(s)):
M
SDi ( s ) M 1 SD(k ) (2)
k 1
Where k is the number of the epoch in the i-th lead; k 1, M ; M is the total num-
ber of epochs in the i-th lead; i 1, P ; P is the total number of EEG leads.
The epochs (EOG artifacts) for which SD(j)>SD(s) (1, 2) in at least one of the eye
leads are removed in all EEG leads.
The algorithm is implemented in the MATLAB that enables obtaining the follow-
ing information about the localized EOG artifacts from a multichannel signal of EEG:
the number of artifacts, a graphical representation of the localized artifacts and the
reconstructed purified EEG.
The proposed algorithm was tested on EEG records collected at the Tver State Tech-
nical University. The test EEG records were obtained from the experiments that took
place during the study on human cognitive activity. The testees were 20 people be-
tween the age of 18 to 27 years old. The obtained database of samples included 600
EEG records of 10 seconds each.
The research instrumentation is a hardware and software tool that includes several
personal computers with suitable software and a computer encephalograph “Encepha-
lan-131-03” connected to them (Medicom MTD Ltd, Taganrog, Russia).
EEG signals were recorded according to the international 10–20 system; the re-
cording was made by 19 leads: O2-A2, O1-A1, P4-A2, P3-A1, C4-A2, C3-A1, F4-
A2, F3-A1, Fp2-A2, Fp1-A1, T6-A2, T5-A1, T4-A2, T3-A1, F8-A2, F7-A1, Pz-A1,
Cz-A2, Fz-A1. EEG records were saved in .EEG and .ASCII formats with sampling
frequency 250 Hz and lasting 5 minutes.
The obtained EEG samples were analyzed by three neurophysiologists. The experts
identified EOG artifacts (blinking and eye movement) of different activity in each
EEG. According to the analysis results, each EEG sample contained from 1 to 6 EOG
artifacts.
3 Results and Discussion
Figure 2 shows an example of an initial EEG lasting 2500 readings (10 seconds). The
sample contains EOG artifacts in eye leads (Fp1-A1 (left) and Fp2-A2 (right)), de-
tected by an expert. Epochs 2, 5, and 9 are noted by experts as containing ocular arti-
facts.
Fig. 1. The algorithm for automatic localization of EOG artifacts.
Fig. 2. The initial EEG.
Table 1 presents the algorithm output.
Table 1. The results of EOG artifacts localization in one EEG sample
Epoch 1 2 3 4 5 6 7 8 9 10
Lead Fp1-A1
SD (j) 78.3 199.2 71.6 47.3 164.3 82.7 60.5 54.1 264.4 58.8
SD (s) 108.1
Lead Fp2-A2
SD (j) 76.7 196.0 57.2 78.6 123.9 57.5 48.4 78.1 217.0 57.6
SD (s) 99.1
Artifact
no yes no no yes no no no yes no
localized
Figure 3 shows graphic representations of the reconstructed artifact-free EEG sig-
nal and localized EOG artifacts.
a b
Fig. 3. The reconstructed purified EEG (a) and localized EOG artifacts (b).
After processing all 600 EEG samples, the algorithm successfully localized 96% of
the EOG artifacts noted by experts. Thus, 600 artifact-free EEG fragments were ob-
tained, the minimum fragment duration was 4 seconds.
The algorithm was unable to identify 4% of the artifacts noted by experts. This
happened with EEG samples, which contained the largest number of artifacts (6 of 10
epochs in the sample had artifacts). The SD(s) value in this case was overestimated
and the algorithm considered the epochs with artifacts to be clean.
The proposed algorithm has a disadvantage as it leads losing EEG sections due to
cutting out the entire section of a multichannel signal, even though EOG artifacts are
mostly manifested in the Fp1-A1 and Fp2-A2 eye leads.
The further development of the algorithm is seen in the field of artifact detection
by frequency features.
4 Conclusion
In this paper we consider an algorithm for automatic localizing EOG artifacts that is
based on the correlation of the amplitudes SD for each eye EEG lead. The proposed
algorithm allows detecting artifacts as well as restoring a purified signal with high
reliability. The efficiency of the algorithm for detecting artifacts of EOG has been
confirmed when applied to real EEG signals.
The proposed algorithm can be used for preliminary processing of EEG signals.
5 Acknowledgments
The research has been done within the framework of the grant of the President of
the Russian Federation for state support of young Russian PhD scientists (МК-
1398.2020.9).
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