Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/133606
Citations
Scopus Web of Science® Altmetric
?
?
Type: Journal article
Title: Identification of EEG dynamics during freezing of gait and voluntary stopping in patients with Parkinson's disease
Author: Cao, Z.
John, A.R.
Chen, H.T.
Martens, K.E.
Georgiades, M.
Gilat, M.
Nguyen, H.T.
Lewis, S.J.G.
Lin, C.T.
Citation: IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021; 29:1774-1783
Publisher: IEEE
Issue Date: 2021
ISSN: 1534-4320
1558-0210
Statement of
Responsibility: 
Zehong Cao, Alka Rachel John, Hsiang-Ting Chen, Kaylena Ehgoetz Martens, Matthew Georgiades, Moran Gilat, Hung T. Nguyen, Simon J.G. Lewis, and Chin-Teng Lin
Abstract: Mobility is severely impacted in patients with Parkinson's disease (PD), who often experience involuntary stopping from the freezing of gait (FOG). Understanding the neurophysiological difference between "voluntary stopping" and "involuntary stopping" caused by FOG is vital for the detection of and potential intervention for FOG in the daily lives of patients. This study characterised the electroencephalographic (EEG) signature associated with FOG in contrast to voluntary stopping. The protocol consisted of a timed up-and-go (TUG) task and an additional TUG task with a voluntary stopping component, where participants reacted to verbal "stop" and "walk" instructions by voluntarily stopping or walking. Event-related spectral perturbation (ERSP) analysis was performed to study the dynamics of the EEG spectra induced by different walking phases, including normal walking, voluntary stopping and episodes of involuntary stopping (FOG), as well as the transition windows between normal walking and voluntary stopping or FOG. These results demonstrate for the first time that the EEG signal during the transition from walking to voluntary stopping is distinguishable from that during the transition to involuntary stopping caused by FOG. The EEG signature of voluntary stopping exhibits a significantly decreased power spectrum compared with that of FOG episodes, with distinctly different patterns in the delta and low-beta power in the central area. These findings suggest the possibility of a practical EEG-based tool that can accurately predict FOG episodes, excluding the potential confounding of voluntary stopping.
Keywords: EEG dynamics; freezing of gait; Parkinson’s disease; voluntary stopping
Rights: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
DOI: 10.1109/TNSRE.2021.3107106
Grant ID: http://purl.org/au-research/grants/arc/DP180100656
http://purl.org/au-research/grants/arc/DP210101093
http://purl.org/au-research/grants/arc/DE220100265
Published version: http://dx.doi.org/10.1109/tnsre.2021.3107106
Appears in Collections:Computer Science publications

Files in This Item:
File Description SizeFormat 
hdl_133606.pdfPublished Version2.93 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.