Human electrocortical dynamics while stepping over obstacles

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Abstract

To better understand human brain dynamics during visually guided locomotion, we developed a method of removing motion artifacts from mobile electroencephalography (EEG) and studied human subjects walking and running over obstacles on a treadmill. We constructed a novel dual-layer EEG electrode system to isolate electrocortical signals, and then validated the system using an electrical head phantom and robotic motion platform. We collected data from young healthy subjects walking and running on a treadmill while they encountered unexpected obstacles to step over. Supplementary motor area and premotor cortex had spectral power increases within ~200 ms after object appearance in delta, theta, and alpha frequency bands (3–13 Hz). That activity was followed by similar posterior parietal cortex spectral power increase that decreased in lag time with increasing locomotion speed. The sequence of activation suggests that supplementary motor area and premotor cortex interrupted the gait cycle, while posterior parietal cortex tracked obstacle location for planning foot placement nearly two steps ahead of reaching the obstacle. Together, these results highlight advantages of adopting dual-layer mobile EEG, which should greatly facilitate the study of human brain dynamics in physically active real-world settings and tasks.

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Unveiling the Biometric Potential of Finger-Based ECG Signals

Rafael André Lourenço, Hugo Silva, Ana Fred … (2011)

The ECG signal has been shown to contain relevant information for human identification. Even though results validate the potential of these signals, data acquisition methods and apparatus explored so far compromise user acceptability, requiring the acquisition of ECG at the chest. In this paper, we propose a finger-based ECG biometric system, that uses signals collected at the fingers, through a minimally intrusive 1-lead ECG setup recurring to Ag/AgCl electrodes without gel as interface with the skin. The collected signal is significantly more noisy than the ECG acquired at the chest, motivating the application of feature extraction and signal processing techniques to the problem. Time domain ECG signal processing is performed, which comprises the usual steps of filtering, peak detection, heartbeat waveform segmentation, and amplitude normalization, plus an additional step of time normalization. Through a simple minimum distance criterion between the test patterns and the enrollment database, results have revealed this to be a promising technique for biometric applications.

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Removal of movement artifact from high-density EEG recorded during walking and running.

Clifford Ferris, Klaus Gramann, Scott Makeig … (2010)

Although human cognition often occurs during dynamic motor actions, most studies of human brain dynamics examine subjects in static seated or prone conditions. EEG signals have historically been considered to be too noise prone to allow recording of brain dynamics during human locomotion. Here we applied a channel-based artifact template regression procedure and a subsequent spatial filtering approach to remove gait-related movement artifact from EEG signals recorded during walking and running. We first used stride time warping to remove gait artifact from high-density EEG recorded during a visual oddball discrimination task performed while walking and running. Next, we applied infomax independent component analysis (ICA) to parse the channel-based noise reduced EEG signals into maximally independent components (ICs) and then performed component-based template regression. Applying channel-based or channel-based plus component-based artifact rejection significantly reduced EEG spectral power in the 1.5- to 8.5-Hz frequency range during walking and running. In walking conditions, gait-related artifact was insubstantial: event-related potentials (ERPs), which were nearly identical to visual oddball discrimination events while standing, were visible before and after applying noise reduction. In the running condition, gait-related artifact severely compromised the EEG signals: stable average ERP time-courses of IC processes were only detectable after artifact removal. These findings show that high-density EEG can be used to study brain dynamics during whole body movements and that mechanical artifact from rhythmic gait events may be minimized using a template regression procedure.

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Motor learning elicited by voluntary drive.

Silke Anders, Christoph Braun, Leonardo Cohen … (2003)

Motor training consisting of voluntary movements leads to performance improvements and results in characteristic reorganizational changes in the motor cortex. It has been proposed that repetition of passively elicited movements could also lead to improvements in motor performance. In this study, we compared behavioural gains, changes in functional MRI (fMRI) activation in the contralateral primary motor cortex (cM1) and in motor cortex excitability measured with transcranial magnetic stimulation (TMS) after a 30 min training period of either voluntarily (active) or passively (passive) induced wrist movements, when alertness and kinematic aspects of training were controlled. During active training, subjects were instructed to perform voluntary wrist flexion-extension movements of a specified duration (target window 174-186 ms) in an articulated splint. Passive training consisted of wrist flexion- extension movements elicited by a torque motor, of the same amplitude and duration range as in the active task. fMRI activation and TMS parameters of motor cortex excitability were measured before and after each training type. Motor performance, measured as the number of movements that hit the target window duration, was significantly better after active than after passive training. Both active and passive movements performed during fMRI measurements activated cM1. Active training led to more prominent increases in (i) fMRI activation of cM1; (ii) recruitment curves (TMS); and (iii) intracortical facilitation (TMS) than passive training. Therefore, a short period of active motor training is more effective than passive motor training in eliciting performance improvements and cortical reorganization. This result is consistent with the concept of a pivotal role for voluntary drive in motor learning and neurorehabilitation.

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Author and article information

Contributors

Andrew D. Nordin: andrew.nordin@bme.ufl.edu

Journal

Journal ID (nlm-ta): Sci Rep

Journal ID (iso-abbrev): Sci Rep

Title: Scientific Reports

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2045-2322

Publication date (Electronic): 18 March 2019

Publication date PMC-release: 18 March 2019

Publication date Collection: 2019

Volume: 9

Electronic Location Identifier: 4693

Affiliations

[1 ]ISNI 0000 0004 1936 8091, GRID grid.15276.37, J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, ; Gainesville, USA

[2 ]ISNI 0000 0001 2151 958X, GRID grid.420282.e, Human Research and Engineering Directorate, U.S. Army Research Laboratory, ; Aberdeen Proving Ground, USA

Article

Publisher ID: 41131

DOI: 10.1038/s41598-019-41131-2

PMC ID: 6423113

PubMed ID: 30886202

SO-VID: 7efd25cf-6c4a-48c7-8ea2-c0244750978a

License:

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/.

History

Date received : 30 August 2018

Date accepted : 28 February 2019

Funding

Funded by: FundRef https://doi.org/10.13039/100006754, United States Department of Defense | United States Army | U.S. Army Research, Development and Engineering Command | Army Research Laboratory (U.S. Army Research Laboratory);

Award ID: ARL W911NF-10-2-0022

Award Recipient : Daniel P. Ferris

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Human electrocortical dynamics while stepping over obstacles

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Most cited references 72

Unveiling the Biometric Potential of Finger-Based ECG Signals

Removal of movement artifact from high-density EEG recorded during walking and running.

Motor learning elicited by voluntary drive.

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Cited by 45

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