Performance-based approach for movement artifact removal from electroencephalographic data recorded during locomotion

Evyatar Arad, Ronny P. Bartsch, Jan W. Kantelhardt, Meir Plotnik

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

Abstract

The appreciation for the need to record electroencephalographic (EEG) signals from humans while walking has been steadily growing in recent years, particularly in relation to understanding gait disturbances. Movement artefacts (MA) in EEG signals originate from mechanical forces applied to the scalp electrodes, inducing small electrode movements relative to the scalp which, in turn, cause the recorded voltage to change irrespectively of cortical activity. These mechanical forces, and thus MA, may have various sources (e.g., ground reaction forces, head movements, etc.) that are inherent to daily activities, notably walking. In this paper we introduce a systematic, integrated methodology for removing MA from EEG signals recorded during treadmill (TM) and over-ground (OG) walking, as well as quantify the prevalence of MA in different locomotion settings. In our experiments, participants performed walking trials at various speeds both OG and on a TM while wearing a 32-channel EEG cap and a 3-axis accelerometer, placed on the forehead. Data preprocessing included separating the EEG signals into statistically independent additive components using independent component analysis (ICA). We observed an increase in electro-physiological signals (e.g., neck EMG activations for stabilizing the head during heel-strikes) as the walking speed increased. These artefact independent-components (ICs), while not originating from electrode movement, still exhibit a similar spectral pattern to the MA ICs–a peak at the stepping frequency. MA was identified and quantified in each component using a novel method that utilizes the participant’s stepping frequency, derived from a forehead-mounted accelerometer. We then benchmarked the EEG data by applying newly established metrics to quantify the success of our method in cleaning the data. The results indicate that our approach can be successfully applied to EEG data recorded during TM and OG walking, and is offered as a unified methodology for MA removal from EEG collected during gait trials.

Original languageEnglish
Article numbere0197153
JournalPLoS ONE
Volume13
Issue number5
DOIs
StatePublished - May 2018

Bibliographical note

Publisher Copyright:
© 2018 Arad et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding

We acknowledge financial support from the German Israeli Foundation for Scientific Research and Development (Grant numbers I-1298-415.13/2015 to MP and I-1372-303.7/2016 to RPB) (http://www.gif.org.il/Pages/default.aspx); the Ministry of Science and Technology (Grant # 3-12072 to MP) (http://archive.most.gov.il/English/ Pages/default.aspx); and the Israel Science Foundation (Grant numbers 1657-16 to MP and 1657/16 to RPB) (https://www.isf.org.il/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank Ms. Livnat Corelenchtein for her assistance in data collection and analysis, as well as Mr. Amihai Gottlieb for helping with the statistical analysis. We also thank the participants of this study.

FundersFunder number
German-Israeli Foundation for Scientific Research and DevelopmentI-1372-303.7/2016, I-1298-415.13/2015
Israel Science Foundation1657-16
Ministerio de Ciencia y Tecnología3-12072

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