High-density EEG mobile brain/body imaging data recorded during a challenging auditory gait pacing task

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Abstract

In this report we present a mobile brain/body imaging (MoBI) dataset that allows study of source-resolved cortical dynamics supporting coordinated gait movements in a rhythmic auditory cueing paradigm. Use of an auditory pacing stimulus stream has been recommended to identify deficits and treat gait impairments in neurologic populations. Here, the rhythmic cueing paradigm required healthy young participants to walk on a treadmill (constant speed) while attempting to maintain step synchrony with an auditory pacing stream and to adapt their step length and rate to unanticipated shifts in tempo of the pacing stimuli (e.g., sudden shifts to a faster or slower tempo). High-density electroencephalography (EEG, 108 channels), surface electromyography (EMG, bilateral tibialis anterior), pressure sensors on the heel (to register timing of heel strikes), and goniometers (knee, hip, and ankle joint angles) were concurrently recorded in 20 participants. The data is provided in the Brain Imaging Data Structure (BIDS) format to promote data sharing and reuse, and allow the inclusion of the data into fully automated data analysis workflows.

Abstract

Measurement(s)	brain activity measurement • electric potential • heel strike • joint angle • auditory stimulation
Technology Type(s)	electroencephalography (EEG) • electromyography • Pressure Sensor Device • goniometer • computer
Sample Characteristic - Organism	Homo sapiens

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.9919376

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Most cited references 41

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Dual tasking, gait rhythmicity, and Parkinson's disease: which aspects of gait are attention demanding?

Galit Yogev, Nir Giladi, Chava Peretz … (2005)

Cognitive function and the performance of a secondary, dual task may affect certain aspects of gait, but the relationships between cognitive function and gait are not well understood. To better understand the motor control of gait and the relationship between cognitive function and gait, we studied cognitive function and the effects of different types of dual tasking on the gait of patients with Parkinson's disease (PD) and controls, contrasting measures of gait automaticity and rhythmicity with other features. Patients with idiopathic PD (n=30; mean age 71.8 year) with moderate disease severity (Hoehn and Yahr Stage 2--3) were compared to age and gender-matched healthy controls (n=28). Memory and executive function were also assessed. In both groups, gait speed decreased in response to dual tasking, in a parallel fashion. For the PD group only, gait variability increased compared to usual walking. Executive function was significantly worse in the PD group, while memory was not different in the two groups. Executive function measures were significantly correlated with gait variability during dual tasking, but not during usual walking. These findings demonstrate that regulation of gait variability and rhythmicity is apparently an automatic process that does not demand attention in healthy adults. In patients with PD, however, this ability becomes attention-demanding and worsens when subjects perform secondary tasks. Moreover, the associations between executive function and gait variability suggest that a decline in executive function in PD may exacerbate the effects of dual tasking on gait, potentially increasing fall risk.

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EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing

Arnaud Delorme, Tim Mullen, Christian Kothe … (2011)

We describe a set of complementary EEG data collection and processing tools recently developed at the Swartz Center for Computational Neuroscience (SCCN) that connect to and extend the EEGLAB software environment, a freely available and readily extensible processing environment running under Matlab. The new tools include (1) a new and flexible EEGLAB STUDY design facility for framing and performing statistical analyses on data from multiple subjects; (2) a neuroelectromagnetic forward head modeling toolbox (NFT) for building realistic electrical head models from available data; (3) a source information flow toolbox (SIFT) for modeling ongoing or event-related effective connectivity between cortical areas; (4) a BCILAB toolbox for building online brain-computer interface (BCI) models from available data, and (5) an experimental real-time interactive control and analysis (ERICA) environment for real-time production and coordination of interactive, multimodal experiments.

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Comfortable and maximum walking speed of adults aged 20-79 years: reference values and determinants.

Richard Bohannon (1996)

to establish reference values for both comfortable and maximum gait speed and to describe the reliability of the gait speed measures and the correlation of selected variables with them. descriptive and cross-sectional. subjects were 230 healthy volunteers. Gait was timed over a 7.62 m expanse of floor. Actual and height normalized speed were determined. Lower extremity muscle strength was measured with a hand-held dynamometer. mean comfortable gait speed ranged from 127.2 cm/s for women in their seventies to 146.2 cm/s for men in their forties. Mean maximum gait speed ranged from 174.9 cm/s for women in their seventies to 253.3 cm/s for men in their twenties. Both gait speed measures were reliable (coefficients > or = 0.903) and correlated significantly with age (r > or = -0.210), height (r > or = 0.220) and the strengths of four measured lower extremity muscle actions (r = 0.190-0.500). The muscle action strengths most strongly correlated with gait speed were nondominant hip abduction (comfortable speed) and knee extension (maximum speed). these normative values should give clinicians a reference against which patient performance can be compared in a variety of settings. Gait speed can be expected to be reduced in individuals of greater age and of lesser height and lower extremity muscle strength.

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Author and article information

Contributors

Johanna Wagner: j9wagner@ucsd.edu

Journal

Journal ID (nlm-ta): Sci Data

Journal ID (iso-abbrev): Sci Data

Title: Scientific Data

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2052-4463

Publication date (Electronic): 17 October 2019

Publication date PMC-release: 17 October 2019

Publication date Collection: 2019

Volume: 6

Electronic Location Identifier: 211

Affiliations

[1 ]ISNI 0000 0001 2107 4242, GRID grid.266100.3, Swartz Center for Computational Neuroscience, , Institute for Neural Computation, University of California San Diego, ; La Jolla, CA USA

[2 ]ISNI 0000 0001 2107 4242, GRID grid.266100.3, Electric and Computer Engineering Department, Jacobs School of Engineering, , University of California San Diego, ; La Jolla, CA USA

[3 ]ISNI 0000 0001 2294 748X, GRID grid.410413.3, Laboratory for Brain Computer Interfaces, , Institute of Neural Engineering, Graz University of Technology, ; Graz, Austria

[4 ]ISNI 0000000121539003, GRID grid.5110.5, Department of Psychology, , University of Graz, ; Graz, Austria

[5 ]Department of Rehabilitation, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands

Author information

Gernot Mueller-Putz http://orcid.org/0000-0002-0087-3720

Article

Publisher ID: 223

DOI: 10.1038/s41597-019-0223-2

PMC ID: 6797727

PubMed ID: 31624252

SO-VID: 96346a6c-e7e5-46d4-99c9-6e0e54908dd3

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article.

History

Date received : 19 June 2019

Date accepted : 6 September 2019

Funding

Funded by: Graz University of Technology, Austria, Faculty of Computer Science, Future Labs Reloaded 2013; Austrian Federal Ministry for Science, Research and Economy, Marietta Blau Grant; gifts to UCSD from The Swartz Foundation (Sag Harbor NY).

Funded by: FundRef https://doi.org/10.13039/100000002, U.S. Department of Health & Human Services | National Institutes of Health (NIH);

Award ID: 5R01-NS047293-13

Award Recipient : Ramon Martinez-Cancino Arnaud Delorme Scott Makeig

Funded by: U.S. Department of Health & Human Services | National Institutes of Health (NIH)

Funded by: The Swartz Foundation (Old Field, NY), gift to UCSD

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Keywords: cognitive control,sensorimotor processing,electroencephalography - eeg

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Keywords: cognitive control, sensorimotor processing, electroencephalography - eeg

High-density EEG mobile brain/body imaging data recorded during a challenging auditory gait pacing task

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Communication Through Coherence

Most cited references 41

Dual tasking, gait rhythmicity, and Parkinson's disease: which aspects of gait are attention demanding?

EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing

Comfortable and maximum walking speed of adults aged 20-79 years: reference values and determinants.

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