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

Abstract

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. 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. Currently, the most common methods for removing artifacts in movement studies are methods based on independent component analysis (ICA). 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. 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.

A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics

“…This is another reason, why state-of-the-art methods would likely be more appropriate. Combining EEG with other control signals, such as EMG, in a hybrid BCI system (Banville and Falk, 2016 ; Wöhrle et al, 2017 ; Li et al, 2019 ; Hooda et al, 2020 ; Tortora et al, 2020c ), and further developing artifact removal methods (Gorjan et al, 2022 ), could prove necessary to achieve the balance in reliability and decoding speed that are required for real-world BCI control. Therefore, investigating real-time decoding with state-of-the-art models is essential to move toward practical BCI decoding in real-world applications.…”

“…One of such mechanisms is the movement-related cortical potential (MRCP), which can be observed from the EEG signal when performing a movement (Shakeel et al, 2015;Olsen et al, 2021). Detection of these MRCPs can be used to decode the onset of lower-limb movements for BCI control (Liu et al, 2018;Marusic et al, 2022). However, the involved brain structures and resulting neural activity related to movement tend to adapt after amputation due to neuroplasticity (Molina-Rueda et al, 2019).…”

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“…Artifacts from the first class are problematic for ICA because since their spatial distribution is extremely variable, they introduce a large number of unique scalp maps, leaving few ICs available for capturing brain sources. The data streams were therefore processed with a combination of ICA and artifact subspace reconstruction (ASR) which has several advantages including the automated removal of artifact components, its usability for online applications, and the ability to remove transient or large-amplitude artifacts that the ICA method struggles with (Kothe and Jung, 2014 ; Chang et al, 2018 ; Gorjan et al, 2022 ).…”

Quantifying the impact and profiling functional EEG artifacts

“…The term artifact removal (or correction) encompasses methods concerned with the "cleaning" of artifact-contaminated EEG signal intervals so as to keep them available in the processing pipeline for continuous, uninterrupted BCI, rather than only isolating these intervals and excluding them from further processing (artifact rejection) [5]. A series of surveys capture the gradual (including recent) progress, the increasing algorithmic elaborateness and pervasiveness, and the overall vast amount of work dedicated to this topic up to this day [3], [6]- [14].…”

“…Furthermore, even where fully-automatic approaches are pursued, there is no consensus among different studies on the optimal criteria that can be used to reveal the presence and influence of artifacts; similarly, the hyperparameters (e.g., thresholds applied on the different metrics) are rather arbitrary and not guaranteed to generalize [5], [16]. Another two issues emerging from the recent literature [9], [12]- [14] are the relative lack of universal and holistic EEG artifact studies, with most works focusing exclusively on eye-, muscle-or movement-related artifacts), and the related issue that this research has investigated the differences between potential sources of artifact, but not the "functions" (i.e., the human actions and activities) that create them. Because of this, it is still hard to infer what the real impact of different kinds of artifacts would be, especially in real-world settings.…”