Paper Info
Title
Directed neural connectivity changes in robot-assisted gait training: a partial Granger causality analysis.
Abstract
Now-a-days robotic exoskeletons are often used to help in gait training of stroke patients. However, such robotic systems have so far yielded only mixed results in benefiting the clinical population. Therefore, there is a need to investigate how gait learning and de-learning get characterised in brain signals and thus determine neural substrate to focus attention on, possibly, through an appropriate brain-computer interface (BCI). To this end, this paper reports the analysis of EEG data acquired from six healthy individuals undergoing robot-assisted gait training of a new gait pattern. Time-domain partial Granger causality (PGC) method was applied to estimate directed neural connectivity among relevant brain regions. To validate the results, a power spectral density (PSD) analysis was also performed. Results showed a strong causal interaction between lateral motor cortical areas. A frontoparietal connection was found in all robot-assisted training sessions. Following training, a causal "top-down" cognitive control was evidenced, which may indicate plasticity in the connectivity in the respective brain regions.
Year
DOI
Venue
2014
10.1109/EMBC.2014.6945083
EMBC
Field
DocType
Volume
Population,Neuroscience,Gait,Computer science,Brain–computer interface,Granger causality,Artificial intelligence,Exoskeleton,Physical medicine and rehabilitation,Cognition,Computer vision,Gait training,Neural substrate
Conference
2014
ISSN
Citations 
PageRank 
1557-170X
2
0.42
References 
Authors
7
7
Name
Order
Citations
PageRank
Vahab Youssofzadeh120.42
Damiano Zanotto2111.89
Paul Stegall320.42
Muhammad Naeem422.79
KongFatt Wong-Lin54611.52
Sunil K Agrawal620.42
Girijesh Prasad720.42