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

PloS One
Evyatar AradMeir Plotnik

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 ...Continue Reading

References

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Citations

Dec 14, 2018·Journal of Neural Engineering·Steven M Peterson, Daniel P Ferris
Mar 12, 2020·Journal of Clinical Medicine·Federico GennaroEling D de Bruin
Sep 11, 2020·The European Journal of Neuroscience·Nadine Svenja Josée JacobsenStefan Debener
Jun 21, 2019·Journal of Neural Engineering·Atilla Kilicarslan, Jose Luis Contreras Vidal
Oct 28, 2019·Brain : a Journal of Neurology·Daniel WeissSimon J G Lewis
Dec 6, 2019·Frontiers in Human Neuroscience·Christoph TremmelDean J Krusienski
May 21, 2019·Frontiers in Human Neuroscience·Sarah BlumStefan Debener
Dec 24, 2021·Movement Disorders : Official Journal of the Movement Disorder Society·Nicolaas I BohnenUNKNOWN MDS-Scientific Issues Committee

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Methods Mentioned

BETA
PCA

Software Mentioned

SPSS
EEGLAB
MATLAB
ASR
AMICA
PREP
CleanLine

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