Cargando…
Performance-based approach for movement artifact removal from electroencephalographic data recorded during locomotion
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...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5955591/ https://www.ncbi.nlm.nih.gov/pubmed/29768471 http://dx.doi.org/10.1371/journal.pone.0197153 |
_version_ | 1783323749178671104 |
---|---|
author | Arad, Evyatar Bartsch, Ronny P. Kantelhardt, Jan W. Plotnik, Meir |
author_facet | Arad, Evyatar Bartsch, Ronny P. Kantelhardt, Jan W. Plotnik, Meir |
author_sort | Arad, Evyatar |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5955591 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-59555912018-05-25 Performance-based approach for movement artifact removal from electroencephalographic data recorded during locomotion Arad, Evyatar Bartsch, Ronny P. Kantelhardt, Jan W. Plotnik, Meir PLoS One Research Article 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. Public Library of Science 2018-05-16 /pmc/articles/PMC5955591/ /pubmed/29768471 http://dx.doi.org/10.1371/journal.pone.0197153 Text en © 2018 Arad et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Arad, Evyatar Bartsch, Ronny P. Kantelhardt, Jan W. Plotnik, Meir Performance-based approach for movement artifact removal from electroencephalographic data recorded during locomotion |
title | Performance-based approach for movement artifact removal from electroencephalographic data recorded during locomotion |
title_full | Performance-based approach for movement artifact removal from electroencephalographic data recorded during locomotion |
title_fullStr | Performance-based approach for movement artifact removal from electroencephalographic data recorded during locomotion |
title_full_unstemmed | Performance-based approach for movement artifact removal from electroencephalographic data recorded during locomotion |
title_short | Performance-based approach for movement artifact removal from electroencephalographic data recorded during locomotion |
title_sort | performance-based approach for movement artifact removal from electroencephalographic data recorded during locomotion |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5955591/ https://www.ncbi.nlm.nih.gov/pubmed/29768471 http://dx.doi.org/10.1371/journal.pone.0197153 |
work_keys_str_mv | AT aradevyatar performancebasedapproachformovementartifactremovalfromelectroencephalographicdatarecordedduringlocomotion AT bartschronnyp performancebasedapproachformovementartifactremovalfromelectroencephalographicdatarecordedduringlocomotion AT kantelhardtjanw performancebasedapproachformovementartifactremovalfromelectroencephalographicdatarecordedduringlocomotion AT plotnikmeir performancebasedapproachformovementartifactremovalfromelectroencephalographicdatarecordedduringlocomotion |