Removal of movement-induced EEG artifacts: current state of the art and guidelines [original]

J. Neural Eng. 19 (2022) 011004 https://doi.org/10.1088/1741-2552/ac542c

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TOPICAL REVIEW

Removal of movement-induced EEG artifacts: current state of the

art and guidelines

Dasa Gorjan1, Klaus Gramann2, Kevin De Pauw3,4and Uros Marusic1,5,∗

1Science and Research Centre Koper, Institute for Kinesiology Research, Koper, Slovenia

2Biological Psychology and Neuroergonomics, Technische Universitaet Berlin, Berlin, Germany

3Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, Brussels, Belgium

4Brussels Human Robotics Research Center (BruBotics), Vrije Universiteit Brussel, Brussels, Belgium

5Department of Health Sciences, Alma Mater Europaea—ECM, Maribor, Slovenia

∗Author to whom any correspondence should be addressed.

E-mail: [email protected] and [email protected]

Keywords: mobile brain/body imaging, EEG, locomotion, movement artifacts, independent component analysis

Abstract

Objective: Electroencephalography (EEG) is a non-invasive technique used to record cortical

neurons’ electrical activity using electrodes placed on the scalp. It has become a promising avenue

for research beyond state-of-the-art EEG research that is conducted under static conditions. EEG

signals are always contaminated by artifacts and other physiological signals. Artifact contamination

increases with the intensity of movement. Approach: In the last decade (since 2010), researchers

have started to implement EEG measurements in dynamic setups to increase the overall ecological

validity of the studies. Many different methods are used to remove non-brain activity from the

EEG signal, and there are no clear guidelines on which method should be used in dynamic setups

and for specific movement intensities. Main results: Currently, the most common methods for

removing artifacts in movement studies are methods based on independent component analysis.

However, the choice of method for artifact removal depends on the type and intensity of

movement, which affects the characteristics of the artifacts and the EEG parameters of interest.

When dealing with EEG under non-static conditions, special care must be taken already in the

designing period of an experiment. Software and hardware solutions must be combined to achieve

sufficient removal of unwanted signals from EEG measurements. Significance: We have provided

recommendations for the use of each method depending on the intensity of the movement and

highlighted the advantages and disadvantages of the methods. However, due to the current gap in

the literature, further development and evaluation of methods for artifact removal in EEG data

during locomotion is needed.

List of abbreviations

AMICA adaptive mixture independent

component analysis

ASR artifact subspace separation

BCI brain–computer interface

BSS blind source separation

CCA canonical correlation analysis

EEG electroencephalography

EEMD ensemble empirical mode

decomposition

ICA independent component analysis

MoBI mobile brain/body imaging

ORICA online recursive independent

component analysis

PCA principal component analysis

RELICA reliable independent component

analysis

J. Neural Eng. 19 (2022) 011004 D Gorjan et al

1. Introduction

EEG is a non-invasive technique used to record

the electrical activity of cortical neurons with elec-

trodes placed on the scalp [1]. EEG amplifiers can be

lightweight and portable while providing high tem-

poral resolution of the recorded signal rendering EEG

the most suitable brain imaging device to measure

human brain activity during locomotion [2]. How-

ever, EEG signals are highly susceptible to artifact

contamination due to their electrical properties [3].

Artifacts can be of mechanical or electrical origin,

such as cable or electrode movements, or the presence

of other electromagnetic devices. Besides that, the

EEG signal is also affected by other physiological sig-

nals, such as eye movements or muscle activity. In tra-

ditional EEG research, these non-brain physiological

contributions to the recorded EEG signal are con-

sidered artifactual as they distort the signal of interest

due to volume and capacitive conduction. Nonethe-

less, eye movement and muscle activity provide addi-

tional information about cognitive processes if ana-

lyzed separately and thus should not be considered

artifacts [4]. These physiological signals originate

mainly from muscle activity, eye movements, and car-

diac activity of the participant which increase with

movement in general and with the intensity of move-

ment specifically. Recording EEG in stationary pos-

itions and a controlled laboratory environment can

result in very clean signals, however, these kinds of

experiments do not lead to a good understanding

of brain dynamics in real-life situations [5]. On the

other hand, movement as part of realistic and natural

behavior increases the occurrence of unwanted sig-

nals in the EEG signal [6]. MoBI [4,7,8] overcomes

these restrictions by combining EEG with motion

tracking and potentially other physiological signals

combined with data-driven analyses techniques to

dissociate brain from non-brain activity. MoBI is thus

a promising approach to investigate human brain

dynamics in actively locomoting humans.

Walking, along with standing and sitting, is the

most important activity of daily living and has

recently been extensively studied with EEG [9–14].

The better understanding of the brain dynamics and

motor control of gait might aid in the control of

lower limb exoskeletons [14], the understanding of

the effect of cognition on gait, or the association

of the neural correlates of altered gait patterns with

pathologies [15]. Measuring EEG during gait is chal-

lenging due to its susceptibility to artifacts [3]. A

study comparing EEG to accelerometer data during

treadmill walking [16] found that EEG and accel-

erometer signals have similar time-frequency prop-

erties up to 150 Hz. In addition, they found that

movement artifacts phase-coupled with the stepping

frequency produce a signal contaminated with up to

15 harmonics. The number of harmonics depends on

the walking speed and location of the electrode. These

results show that gait-related artifacts are complex,

difficult to detect and remove, and that simple solu-

tions such as band-pass filters are not sufficient [17].

The characteristics of gait-related artifacts are

closely related to the biomechanics and the type of

gait. The gait cycle consists of two main phases: swing

phase and stance phase. The stance phase begins with

heel strike and ends with toe-off, the first and last

contact of the foot with the ground, respectively.

In normal gait, the sequence is usually as follows:

right heel strike, left toe-off, left heel strike, right

toe-off. Between right/left heel strike and left/right

toe-off is a double support phase and the rest is a

single support phase [15]. A recent study [18] com-

bined seven features from various data dimensions

to thoroughly characterize gait-related artifacts as a

seven-dimensional footprint. This footprint includes

features of time, time-frequency, spatial, and source

domains. This gait-related artifact characterization

could be used to optimize future preprocessing and

artifact removal pipelines or to compare different

artifact removal methods for EEG data during walk-

ing [18]. An earlier study by Kline et al examined the

characteristics of movement artifacts recorded with

EEG at different walking speeds and they found that

with walking speed increased movement artifact fre-

quency spectra amplitudes and maximal frequency

at which the movement artifact occurred [17]. Addi-

tionally, they found that the head accelerometer data

had poor correlation with the movement artifact on

the EEG electrodes. In general, artifacts are more pro-

nounced in the EEG signal during movement, have

specific characteristics depending on the type and

intensity of movement, and they interfere with the

EEG signal, making it difficult to distinguish them

from brain signals. Some studies raised doubts about

the cortical origin of time-frequency results during

walking due to insufficient artifact removal [16,17].

The most commonly used methods for artifact

removal in movement studies are mainly techniques

based on ICA [19], ASR [20], and CCA [21]. There

are many types of improved versions or combina-

tions of these methods (e.g. AMICA [22] or RELICA

[23] in the case of ICA), and new methods for arti-

fact removal in the EEG domain are constantly being

developed or evaluated. To date, there are no clear

guidelines as to which artifact removal methods are

suitable for specific movement studies. The current

paper bridges this gap in the literature and reviews

the methods currently used in EEG studies involving

human movement.

Since the publication of the first studies using

EEG signals recorded during whole body movement

[2,24], many studies have been conducted invest-

igating walking [12,25,26], cycling [27–29], and

some other types of whole body movement such

as jumping and squatting [30,31]. In the current

study, we focus on walking and cycling, as these are

the most frequently studied. Only a few studies have

J. Neural Eng. 19 (2022) 011004 D Gorjan et al

Figure 1. Diagram of artifact removal methods used in movement studies described in this section.

investigated EEG during running [2,32,33], and

although the feasibility of measuring the signal dur-

ing running was confirmed [2], sometimes research-

ers are unable to use the signal due to artifact con-

tamination [33] or most electrodes and parts of the

data had to be removed [32]. To avoid such cases,

it is important to have appropriate hardware, fol-

low recommendations to minimize artifacts during

measurement (i.e. appropriate size of electrode cap,

preparation of electrodes, testing of signal, removal

of all possible sources of artifacts from the environ-

ment), and use effective methods for artifact removal

after the data have been measured. The lack of EEG

studies during intense movement implies the need to

improve EEG systems to further avoid artifacts and to

develop more efficient methods for artifact removal

and to evaluate existing methods for specific types

and intensities of movement.

This manuscript provides an overview of artifact

removal methods used in walking and cycling studies.

We discuss the most commonly used filtering tech-

niques, BSS methods, and ASR as well as the develop-

ment of new combinations of methods evaluated for

EEG signals during locomotion (figure 1). We sum-

marize these evaluated practices to improve the effi-

ciency of each method on locomotion EEG data. The

goal is to provide recommendations on suitable arti-

fact rejection methods for use in EEG studies with

locomotion of participants, possibly depending on

the intensity of the movement itself.

2. Artifact removal methods used in

movement studies

2.1. Filters

2.1.1. Low and high-pass filters

Low-pass and high-pass filters are commonly used

preceding other artifact removal methods. This type

of filter alone is sufficient only if the frequency bands

of artifacts and signals do not overlap, which is

not the case in studies involving movement [16].

Usually, high-pass filters with cut-off frequencies of

0.1–1 Hz are used in EEG studies, but higher cut-off

frequencies might be indicated in studies involving

fast movements [5,34]. Before performing ICA, it

is recommended to use a high-pass filter to improve

decomposition rather than a low-pass filter. Espe-

cially for higher intensities of movement (e.g. higher

speed of walking), the cut-off frequency can be

higher, up to 2 Hz or even more, as the signals are

more contaminated with artifacts [5]. Similarly, in

adaptive filtering, a high-pass filter with a cut-off fre-

quency of 2 Hz is found to give better results [34].

When using these filters, it is important to know

which frequency range is of interest, as applying a

higher high-pass filter will also remove information in

delta and theta frequencies bands that originate from

the brain.

2.1.2. Adaptive filters

Adaptive filters can adapt to the changing character-

istics of the artifacts by adjusting filter weights or coef-

ficients from one to the next time point according to

the reference signal with an optimization algorithm.

These filters use external sensors as a reference for

artifacts. One of the first adaptive filters were the least

mean squares algorithms which are used to find the

filter coefficients that produce the least mean square

of the error signal (difference between the reference

and the output signal). The goal of these filters is to

find the relationship between the input signal and

the reference signal. Linear mapping of the refer-

ence signal (artifact signal) to the contaminated EEG

signal is insufficient because of the complexity and

dynamics of the signal. Therefore, non-linear filters

are recommended in EEG studies involving move-

ment. Non-linear adaptive filters such as Volterra

[35], bilinear filter classes, cuckoo’s optimization

J. Neural Eng. 19 (2022) 011004 D Gorjan et al

algorithm or alternative approaches can be

used [36].

The advantage of using adaptive filters for arti-

fact removal in EEG signals is that they can be used

in real-time, which is crucial in the case of BCI stud-

ies, and the fast computation can also be beneficial for

offline analysis. The disadvantage of such adaptive fil-

ter approaches is that a good reference signal is neces-

sary to identify artifacts in the EEG signal. There-

fore, an appropriate selection of the reference signal

is important and significantly impacts the outcome of

the adaptive filter. In [35] three-axis head acceleration

values was used as a reference signal, which worked

well for removing movement artifacts from EEG sig-

nals during walking. In [34] a subset of electrode-

tissue impedance components such as magnitude, in-

phase, and quadrature per EEG channel was used for

removing movement artifacts during head shaking

and nodding.

Adaptive filtering can be sufficient for artifact

removal in movement EEG studies, but the selection

of the reference signal, the non-linear relationship

between the EEG signal and the artifact signal, and

optimal algorithm for parameter adaptation process

need to be considered. In [35] it was found that their

algorithm can clean EEG data from 60 electrodes in

real-time during walking at a different speed, but they

used it only on signals filtered between 0.3 and 15 Hz.

In [34] it was found that band-pass filter and adaptive

filtering can substantially reduce movement artifacts

produced by head movement, but they did not eval-

uate the extent to which movement artifacts are still

present in the cleaned signal. Adaptive filters in EEG

studies involving movement are also commonly used

in combination with other artifact removal methods

such as ICA and ASR only to remove ocular arti-

facts [14,37]. Most critically, the described studies

do not investigate whether adaptive filters attenuate

functional neural correlates underlying cognitive pro-

cesses during active behaviors.

2.1.3. Bayesian filters

Bayesian filters use the recorded signal to estimate the

EEG state based on the probability. Thus, if the ori-

ginally recorded data includes artifacts, they will be

part of the probability distribution. Bayesian filters

then use a prediction-correction technique with two

models. The time update model describes how the

state updates from one time point to another. The

measurement model describes how the recorded data

relates to the internal state of the brain. This means

that the algorithm first estimates the state at one time

point and then obtains the feedback as a noisy meas-

urement, which is used to predict a new a priori estim-

ate. These filters work without a reference signal and

can be used online [38]. In EEG studies involving

movement, the Kalman filter is the most commonly

used Bayesian filter, especially for BCI, as the possibil-

ity of real-time application and no additional sensors

for the reference signal are vital for this type of studies.

The main assumption of the Kalman filter is that the

initial uncertainty is Gaussian and that the relation-

ship between the recorded data and the state is linear.

These assumptions prevent the method to capture the

complex relationship between brain and artifactual

activity during dynamic movements. Therefore, an

improved version of the Kalman filter with nonlinear

estimation was developed, called the unscented Kal-

man filter. The latter filter was found to be effective

for BCI applications in movement studies [12,37].

2.2. BSS methods

BSS methods solve the problem of reconstructing

statistically independent sources from a linear mix-

ture without the reference signal or any other prior

knowledge. Due to volume and capacitive conduc-

tion, many different sources are mixed before being

recorded with EEG. Thus, BSS methods have gained

popularity as they estimate the sources from the lin-

ear mixtures measured at the scalp providing insights

into different underlying brain or non-brain gener-

ators. In general, these methods try to find the mix-

ing matrix of different sources and estimate the source

signal only by learning from the data, making differ-

ent assumptions. Usually, it is assumed that the num-

ber of sources is equal to the number of signals, that

the sources are statistically independent and that the

columns in the mixing matrix are linearly independ-

ent [39].

2.2.1. ICA

In recent years, the most popular method for arti-

fact removal in EEG studies, especially in EEG stud-

ies involving movement, has been ICA and other

improved variants based on this method. It is also

the most investigated method in the case of data pre-

processing and comparing its performance on dif-

ferent types of data [40]. Many variations of ICA-

based methods are used in movement studies, such

as InfoMax ICA, fastICA, RELICA, and AMICA

[24,41–44].

The ICA method is solving the BSS problem

by assuming that the signals are a linear mixture

of statistically independent sources associated with

different physiological activities and artifacts. ICA

decomposes the observed signals into independent

components and after removing the unwanted com-

ponents, the clean signal is reconstructed from the

remaining independent components. It separates the

signal with a contrast function based on maximiz-

ing the non-Gaussian similarity and minimizing the

mutual information. Infomax ICA is a variation of the

method using the Infomax algorithm that works as

a line iteration learning algorithm with the contrast

function on the principle of information maximiza-

tion [39]. FastICA is a fast iteration algorithm with

an increased convergence speed. ORICA can estim-

ate the solution of BSS problem in almost real-time

J. Neural Eng. 19 (2022) 011004 D Gorjan et al

and therefore is useful for BCI experiments [45]. REL-

ICA or reliable ICA characterizes statistical reliability

within a dataset of independent components. AMICA

is an asymptotic Newton algorithm used to calcu-

late the maximum likelihood estimate for a mixed

model of independent components. It is a combina-

tion of Infomax and multiple mixture methods [22].

ICA-based algorithms usually provide similar res-

ults, but AMICA generally achieves a more accurate

ICA decomposition, which has been tested on EEG

data from static tasks [19,46]. Additionally, Infomax

ICA and AMICA were tested on EEG datasets during

different exercises (isometric contractions, treadmill

running and ergometer cycling), and AMICA always

performed better or equally well as the Infomax ICA

[47]. AMICA requires more computational power

and time because it learns a more complex model

than other ICA algorithms. The removal of inde-

pendent components that reflect non-brain activity

(e.g. physiological activity or mechanical artifacts) is

not part of the ICA method. Traditionally, unwanted

independent components are manually removed by

an expert in the field. Since automatized removal is

more transparent and less subjective, classifiers are

used to identify and reject artifactual components.

Classifiers are usually pre-trained and do not adapt to

the dataset [48,49]. Thus, if the classifier has not been

trained on data similar to the data being classified, it

may not produce as good results as manual classific-

ation by an expert. Some classifiers also require pre-

recorded artifact sections [50].

ICA algorithms tested at different walking speeds

showed that EEG data recorded at a faster walking

speed are more difficult to clean, to the extent that it

might not be sufficient for faster walking and running

[46]. Additionally, ICA algorithms perform worse

when walking overground in comparison to tread-

mill walking because of higher ground-reaction forces

and inconsistency of stepping frequency when walk-

ing overground, resulting in a quasi-periodic signal

that is more complicated to decompose using the ICA

method [40].

Preparation of the data before using ICA

algorithms is as important or maybe even more

important than the algorithm itself. High-pass fil-

tering of the data before using this method greatly

improves the quality of artifact separation [5,51].

In [51] high-pass filtering was suggested where the

cut-off frequency is just below the frequency band

of interest while simultaneously using a low-pass fil-

ter. In [5] it was shown that for movement studies, a

high-pass filter with a cut-off frequency of at least 1.5

or even 2 Hz should be used before ICA is employed,

depending on the intensity of the movement and the

amount of noise in the signal. However, in contrast

to [51], the authors found no improvement when

using a much higher high-pass frequency. This might

be due to the difference in the low-pass filter set-

tings in the two studies with no low-pass filter in

[5]. The quality of the ICA decomposition is also

affected by the number of channels used. In [33] it

was found that 35 electrodes could be sufficient to

record the two most dominant electrocortical sources

during walking with a concurrent cognitive task, but

they also found that additional electrodes at least up

to 125 improve ICA decomposition. Generally, it is

recommended to use 64 channels or more, and in

movement studies, it is good to increase the num-

ber of channels with increasing movement intensity

[5] to provide higher degrees of freedom for ICA to

explain the increasing numbers of potential artifac-

tual sources. Another method found to improve ICA

decomposition is first cleaning the data with ASR

[52], which we discuss below.

2.2.2. CCA

CCA is another technique that can solve the prob-

lem of BSS. It has been shown to successfully remove

muscle activity and gradient artifact from the brain

signal and to improve the signal-to-noise ratio in EEG

studies [21,53,54]. The occurrence of muscle activ-

ity and gradient artifact in brain signals is more fre-

quent when the subject is moving, therefore CCA can

be useful in movement studies [54,55].

CCA decomposes the sources of signals in a way

that the source components are maximally auto-

correlated and mutually uncorrelated. It is a mul-

tivariate statistical method that maximizes the under-

lying correlation between two multivariate signals.

The first dataset is the recorded EEG signal and the

second dataset is a time-delayed versions of the same

signal. CCA seeks two vectors of weights that pro-

ject the input signals onto two canonical variables

in a way that the canonical correlation is maxim-

ized. Since muscle signals, unlike EEG signals, do not

have high autocorrelation, muscle activity is removed

by setting several of the least autocorrelated source

components to zero before reconstructing the signals.

CCA uses second-order statistics, resulting in lower

computational complexity compared to ICA, which

uses higher-order statistics [56,57]. CCA has shown

to perform better or comparable to ICA in removing

muscle activity [53,54].

There are some improved variants of this method.

For example, multiset CCA extends CCA to more

than two datasets. Instead of maximizing the canon-

ical correlation between two datasets, it attempts to

maximize the overall correlation of several canon-

ical variables with the intention of extracting source

components that are uncorrelated in each dataset but

well correlated across multiple datasets [58]. In [53] it

was shown that CCA increases performance when fol-

lowed by rejection of spectral slope of its components.

They also found that CCA usage is limited to artifact

removal since its components are still mixtures from

different sources. In [54] authors proposed a CCA-

based framework that was evaluated on walking data

and found to be efficient for movement studies. It is

J. Neural Eng. 19 (2022) 011004 D Gorjan et al

recommended to use CCA in combination with other

methods for higher efficiency. The combination with

ICA could be beneficial since then both Gaussian and

non-Gaussian temporally correlated sources could be

separated [53]. Other combinations of CCA has also

been shown to be effective, such as EEMD-CCA [59],

singular spectrum analysis (SSA) [60], or Gaussian

mixture models (GMMs) [61].

2.3. ASR

Another method used in movement studies is ASR

[12,62,63]. The ASR method has several advantages

including the automated removal of artifactual com-

ponents, its usability for online applications, and the

ability to remove transient or large-amplitude arti-

facts that the ICA method struggles with [52]. This

method is relatively new, and its application to move-

ment EEG data is currently poorly evaluated.

ASR is an automatic non-stationary component-

based artifact removal method for removing artifacts

from multi-channel EEG data. It uses a sliding win-

dow on the EEG data and performs PCA decompos-

ition on each window. First, the ASR method auto-

matically extracts reference data from the raw data

based on the distribution of signal variance. Then, it

determines thresholds for artifact component iden-

tification based on the standard deviation across the

principal component space of all windows multiplied

by the cut-off parameter k, which must be defined by

the user. This means that a larger kparameter leads to

no ASR correction; for example, when kis more than

100, less than 3% of the data is modified and when kis

between 5 and 7, 90% of data can be modified. In the

end, the ASR method rejects the artifact components

in each time window if the principal component is

larger than the rejection threshold. Subsequently, the

final reconstruction of the cleaned signals from the

remaining data was computed [20,52]. Reimannian

ASR is an improved version of the ASR method that

uses Reimannian methods for covariance matrices

computation, which has been shown to be beneficial

for artifact removal [64].

In a case study with motor imagery EEG data

[20] it was found that the ASR method with default

parameters is more efficient in artifact removal com-

pared to ICA and PCA methods. In [52] it was found

that for optimal results of the ASR method, a cut-off

parameter between 20 and 30 should be used instead

of default values of 5–7 as previously recommended.

They found that the parameter 5–7 is too aggressive,

which means that brain activity is greatly removed

along with artifacts. When the parameter is less than

20, more brain components than artifact compon-

ents were affected by this method [52]. This could

explain the impressive results of ASR in [20]. In [65]

it was found that the quality of independent compon-

ents calculated after ASR is best with a cut-off para-

meter of 10 or higher which is lower than that in [52],

possibly due to different motor tasks [65]. In [52]

authors demonstrated that ASR is an effective auto-

matic method for artifact removal in EEG data from

attention tasks in a driving simulator, while in [65]

ASR was used with fast walking and single leg stance

EEG data. Further, in [65] it was found that ASR

performed better in motor tasks with more artifact

contamination compared to non-motor tasks. The

drawback of the ASR method is that without aggress-

ive cut-off parameters, it might not be as effective at

removing artifacts such as eye blinks that regularly

occur, and it might not remove movement artifacts

if they are present in the reference data. ASR can be

used in online applications, however, especially for

movement studies that typically use a large number

of channels, one should consider that the computa-

tion time grows quadratically or faster with the num-

ber of channels and one needs to use a longer time

window to compensate for this. In addition, user-

defined reference data is needed to use the method in

real-time [52].

2.4. Combined methods

We have mentioned only the most commonly used

and evaluated methods for artifact removal in move-

ment studies, although there are many other possible

techniques. All have their advantages and disadvant-

ages, and to avoid some of the downsides, combina-

tions of several methods have been proposed. In most

studies, artifact removal has been found to be more

effective when a combination of methods is used than

when only one method is used [60,61,66].

Many combinations with BSS techniques ICA

and CCA with other methods have been evaluated

for different purposes. It was found that ICA com-

bined with spatial filtering as a preprocessing method

(e.g. Laplacian filter, common average rejection fil-

ter) effectively suppresses artifacts even in very small

sets with only three EEG channels [67]. ICA has also

been combined with ASR to improve the quality of

the signal at different walking speeds [68] and to

improve ICA decomposition [52], with ASR as a pre-

processing method for ICA. ICA and CCA methods

are combined into a method called independent vec-

tor analysis. Their complementary exploited statist-

ical information benefits the removal of artifacts [66].

CCA has also been combined with EEMD, which

can be applied to individual channels. First, the

EEMD method is used to decompose a single-channel

signal into a multi-dimensional signal. Then, CCA

isolates the artifact components from the underlying

signal [56,69]. In [70] authors used their version of

EEMD-CCA to remove artifacts in movement stud-

ies with perturbations. A combination of methods

that has been shown to be even more powerful than

EEMD-CCA with multichannel data is CCA and SSA,

with SSA being conducted before CCA. The recom-

mended window length parameter for SSA method is

between 50 and 100. It can take advantage of the mul-

tivariate statistics that SSA is based on, as well as the

J. Neural Eng. 19 (2022) 011004 D Gorjan et al

cross-channel information [60]. Another CCA com-

bination with GMMs was evaluated using GMM after

CCA decomposition to cluster extracted features into

groups to recognize and remove artifacts [61].

3. Summary and guidelines

Based on the reviewed literature on artifact removal

methods in EEG studies involving movement, we

cannot unambiguously conclude which method is

the most appropriate for removing artifacts from

locomotion EEG data, however some guidelines on

which method to choose in specific movement cases

can already be presented. We provide some general

recommendations based on studies that have invest-

igated methods to remove artifacts from EEG data

related to locomotion, which are discussed in this

paper. We summarized the guidelines on how to use

each method depending on the intensity of loco-

motion in table 1.

First, it is important to acknowledge that charac-

teristics of artifacts depend on the type and intens-

ity of movement. The most studied features of arti-

facts are those in EEG data that involve gait [17,18].

These studies found that increased speed of walk-

ing enhances the occurrence of artifacts and increases

specific frequency parameters of artifacts. This shows

that artifact characteristics (e.g. timing and loca-

tion) depend on certain events in gait cycle. Artifact

characteristics of EEG data involving other types of

movement are poorly evaluated, therefore we cannot

provide recommendations depending on the type of

movement (e.g. walking vs cycling).

In locomotion EEG measurements, especially in

high-intensity locomotion, such as fast walking and

running, artifacts and other physiological signals

are more pronounced. Therefore, when recording

and analyzing locomotion EEG data, special atten-

tion must be directed to artifacts starting with the

setup of the experiment. First, the risk of artifacts

can be reduced with good preparation of the parti-

cipant, electrodes, and environment. Next to standard

procedures performed in static EEG measurements

[71,72], it is recommended to set-up the artifact

removal methods preceding data recording because

some methods (e.g. adaptive filters, ASR) require

additional signals of artifacts or baseline periods that

are as clean as possible. The choice of the artifact

removal method depends on the type and intensity

of the movement, which affects the characteristics of

artifacts and EEG parameters of interest [46].

The most common methods for artifact removal

in movement studies are methods based on ICA. To

date, AMICA was found to be the best in stationary

tasks and at least as good as Infomax ICA or better

in treadmill running and ergometer cycling [19,47].

However, not all existing ICA-based methods were

compared to each other, not allowing for a general-

ized conclusion. In low intensity locomotion defined

as slow to normal walking it is recommended to use

a high pass filter with a cut-off frequency around

1.5 Hz and no low-pass filter, while for higher intens-

ities (fast walking or running movement) this fre-

quency can be 2 Hz or more [5]. It is recommended

to use at least 35 electrodes; however, the decompos-

ition improves at least up to 125 electrodes, therefore

for higher intensity locomotion more than 64 elec-

trodes are recommended [5,33]. For ASR it is recom-

mended to use cut-off parameter kfrom 10 to 30 for

low-intensity locomotion, and around 10 for high-

intensity locomotion [52,65]. CCA is a promising

method to remove muscle artifacts, but it is recom-

mended to be used in combination with other meth-

ods (e.g. ICA, SSA, EEMD) to remove artifacts more

thoroughly. All the methods reviewed are commonly

used in combination with each other, which helps

to overcome some disadvantages of the same meth-

ods used alone. Therefore, it is recommended to use

the proposed combined methods when appropriate.

Parametertuning in combined methods should be the

subject of future studies, as this topic has been poorly

investigated.

Another limitation in evaluating different meth-

ods for artifact removal is the lack of an objective

measure to compare the efficiency of the methods,

since the true value of brain activity is unknown. One

way to bypass this problem is to evaluate methods on

simulated data with known true brain activity, where

we can use standard measures such as the signal-to-

noise ratio [73,74]. Simulations of EEG signals have

been improved by using 3D head models, which allow

linear mixing and spatial dependence of signals. How-

ever, some characteristics of the EEG signal are still

difficult to simulate, so evaluation using real data is

also important. On real data, estimation of true brain

activity is used, e.g. by baseline EEG measurements

with minimized artifacts (without movements, eyes

closed…) [47] or by ICA and automatic classification

of independent components is used to evaluate how

many artifactual components are in the signals before

and after artifact removal [52]. Further research is

needed to improve simulation of EEG signals and to

evaluate different objective measures for comparing

artifact removal methods on real data.

To sufficiently remove artifacts, especially in EEG

studies involving high-intensity locomotion, and to

clarify the problem of choosing the artifact removal

method depending on the type and intensity of

movement, different combinations of artifact remov-

ing methods applied to different types of move-

ment data should be further evaluated. Review stud-

ies similar to the current one would guide researchers

through different methods and would help to trans-

parently compare results of different artifact remov-

ing methods and to create pipelines for EEG data

processing. We focused mainly on the algorithms to

remove artifacts in the recorded signals, however joint

hardware and software solution improvements and

J. Neural Eng. 19 (2022) 011004 D Gorjan et al

Table 1. Recommendations for the application of the individual methods for artifact removal depending on the movement intensity (e.g. static: standing, low intensity: slow and normal walking, high intensity: fast walking and

running).

Artifact removal methods

Cyclic movement task

Static Low intensity High intensity

Low and high pass filters Application guidelines In combination with other methods

or when frequency range of interest

is small and not much contaminated

with artifacts (high-pass filter cut-off

frequency: 0.1–1 Hz)

In combination with other methods (high-pass filter cut-off frequency:

>1 Hz/2 Hz or higher as preprocessing for ICA or spatial filtering)

Necessary electrodes Can be used on single channel

Real-time compatibility YES

Adaptive filters Application guidelines Needs reference artifacts signal (depends

on which artifacts you want to remove)

Needs reference artifacts signal (e.g.

three-axis head acceleration)

Needs reference artifacts signal (e.g.

three-axis head acceleration),

Non-linear filters (e.g. Volterra, cuckoo’s

optimization algorithm) are

recommended

Non-linear filters (e.g. Volterra,

cuckoo’s optimization algorithm) are

recommended

Better in combination with other

methods (e.g. ICA, ASR)

Necessary electrodes Can be used on single channel

Real-time compatibility YES

Bayesian filters Application guidelines Kalman filter is recommended Kalman filter/unscented

Kalman filter is recommended

Unscented Kalman filter is recommended

Necessary electrodes Can be used on single channel

Real-time compatibility YES

Infomax ICA Application guidelines Pre-processing with high-pass filter (1.5 Hz cut-off frequency) Pre-processing with high pass filter

(>1.5–2 Hz cut-off frequency)

Necessary electrodes ⩾35 electrodes ⩾64 electrodes

Real-time compatibility NO/some versions of ICA can be used real-time (e.g. ORICA)

AMICA Application guidelines Pre-processing with high pass filter (1.5 Hz cut-off frequency) Pre-processing with high pass filter

(>1.5–2 Hz cut-off frequency)

Necessary electrodes ⩾35 electrodes ⩾64 electrodes

Real-time compatibility NO

(Continued.)

J. Neural Eng. 19 (2022) 011004 D Gorjan et al

Table 1. (Continued.)

Artifact removal methods

Cyclic movement task

Static Low intensity High intensity

CCA Application guidelines Good for muscle artifact removal Good for muscle artifact removal

Better in combination with e.g. ICA, SSA, EEMD

Necessary electrodes Multiple (e.g. 19)/poorly investigated

Real-time compatibility NO

ASR Application guidelines kparameter: 10–30 kparameter: 10–30 kparameter: 10

Better with reference signal Better with reference signal

Good in combination with ICA, AMICA Good in combination with ICA, AMICA

Necessary electrodes Multiple (e.g. 32)/poorly investigated

Real-time compatibility YES with reference signal recorded before measurement

J. Neural Eng. 19 (2022) 011004 D Gorjan et al

implementations are the key to the advancement of

artifact removal in complex high intensity movement

EEG data. Currently, the state-of-the-art hardware

solution is probably a double-layer electrode system,

which includes electrodes recording only movement

artifacts and then removing them from EEG measure-

ments [75]. However, this solution is for now unavail-

able on the market and is therefore not commonly

used.

In conclusion, artifact removal is a crucial pro-

cess to study brain dynamics in a natural everyday

environment or during high-intensity motor activit-

ies. Although the field of artifact removal methods is

rapidly advancing, further evaluation of methods on

locomotion EEG data is needed. Bottom-up recom-

mendations for adjusting the parameters of vari-

ous methods as a function of movement intensity

are formulated. Although we have focused on soft-

ware methods to remove artifacts, software and hard-

ware solutions must be combined to achieve sufficient

removal of unwanted signals from EEG measure-

ments in locomotion or other non-stationary EEG

experimental setups.

Data availability statement

No new data were created or analyzed in this study.

Acknowledgments

This study was supported by the European Union’s

Horizon 2020 research and innovation programme

under grant agreement No952401 (TwinBrain —

TWINning the BRAIN with machine learning for

neuro-muscular efficiency).

Consent for publication

This study is permitted to be submitted and published

in the Journal of Neural Engineering.

Availability of data and materials

Not applicable.

Conflict of interest

None.

ORCID iD

Uros Marusic https://orcid.org/0000-0002-7420-

2137

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