Paper Info
Title
Evaluation and comparison of effective connectivity during simple and compound limb motor imagery.
Abstract
Motor imagery (MI) has been demonstrated beneficial in motor rehabilitation in patients with movement disorders. In contrast with simple limb motor imagery, less work was reported about the effective connectivity networks of compound limb motor imagery which involves several parts of limbs. This work aimed to investigate the differences of information flow patterns between simple limb motor imagery and compound limb motor imagery. Ten subjects participated in the experiment involving three tasks of simple limb motor imagery (left hand, right hand, feet) and three tasks of compound limb motor imagery (both hands, left hand combined with right foot, right hand combined with left foot). The causal interactions among different neural regions were evaluated by Short-time Directed Transfer Function (SDTF). Quite different from the networks of simple limb motor imagery, more effective interactions overlying larger brain regions were observed during compound limb motor imagery. These results imply that there exist significant differences in the patterns of EEG activity flow between simple limb motor imagery and compound limb motor imagery, which present more complex networks and could be utilized in motor rehabilitation for more benefit in patients with movement disorders.
Year
DOI
Venue
2014
10.1109/EMBC.2014.6944720
EMBC
Keywords
Field
DocType
effective connectivity networks,medical disorders,right hand,brain regions,left foot,motor rehabilitation,right foot,electroencephalography,eeg activity flow,short-time directed transfer function,left hand,patient rehabilitation,gait analysis,compound limb motor imagery,brain,movement disorders,sdtf,simple limb motor imagery
Computer vision,Computer science,Cognitive science,Artificial intelligence,Physical medicine and rehabilitation,Motor imagery
Conference
Volume
ISSN
Citations 
2014
1557-170X
0
PageRank 
References 
Authors
0.34
0
10
Name
Order
Citations
PageRank
Weibo Yi103.04
Lixin Zhang221.73
Kun Wang322.07
Xiaolin Xiao4366.57
Feng He501.35
Xin Zhao610.68
Hongzhi Qi74920.61
Peng Zhou8136.25
Baikun Wan910416.90
Dong Ming1010551.47